船舶翻译文献

合集下载

船舶工程外文翻译----船舶结构安全性和可靠性

外文翻译Ship structural safety and reliabilityJ K Paik 1 and P A Frieze 21 Pusan National University, South Korea2 PAFA Consulting Engineers, UKSummaryRecent research and development in the area ofdesign methodologies related to safety andreliability of ship structures is reviewed, focusing onsome relevant probabilistic approaches used for theload and resistance factor design (LRFD) of shiphulls against collapse. Important insights andfindings previously obtained in the literature aresummarized, and recommendations are made withrespect to both technologically improved designprocedures and those needed for futuredevelopment.Key words:ship structural safety; ship structural reliability; load and resistance factor design; ship hull collapse;probability-based ship structural design; hull girder load; hull girder strength; ultimate limit state; ultimate strength;target reliability.Hull girder collapse accidentOverall collapse of ships’ hull s rarely occurs in stillwater or in waves. Fig. 1 shows a Capesize bulk carrierthat recently collapsed during discharge in port of her126 000 t of iron ore cargo. While the 23-year-old139 800 dwt ship has not broken in two, her mid-section is reportedly lying on the seabed, indicatingthat the hull girder has, in fact, completely collapsed.After having emptied the bow and aft holds amongfive cargo holds, buckling collapse took place in thevessel’s deck while the central hold was still full. It isconsidered that this incident happened primarily asa result of improper unloading of the cargo from theship, but there is certainly speculation about the hull’slimit state design as well as the relevance of inspectionand maintenance.Fig. 1 A ship hull collapse during unloading of cargo at port(Courtesy of Lloyds List)A ship hull in the intact condition will clearlysustain applied loads smaller than the design loads,and in normal sea and loading conditions it will notsuffer structural failure such as buckling and collapse,except for possible localized yielding. However, theloads acting on the ship hull are uncertain, owing torough seas or unusual loading/unloading of cargo. Inthese cases, applied loads may exceed design loadsand the ship hull may collapse globally. Furthermore,since aging ships may suffer structural degradationdue to corrosion, fatigue and local damage witha consequential reduction of their structuralresistance, the hull may collapse under applied loadseven smaller than the design loads.Probability-based structural designprocedureThe main steps for probability-based ship structuraldesign are normally as follows:●establish a target reliability;●identify all unfavorable failure modes of thestructure;●formulate the limit state function for each failuremode identified above;●identify the probabilistic characteristics (mean,variance, distribution) of the random variables inthe limit state function;●calculate the reliability against the limit state withrespect to each failure mode of the structure;●assess if the predicted reliability is greater than thetarget reliability, and redesign the structureotherwise;●evaluate the reliability analysis results with respectto parametric sensitivity considerations.Modelling of hull girder strengthFour types of limit state can be considered: namely,serviceability limit state, ultimate limit state, fatiguelimit state and accidental limit state . Theserviceability limit state involves deterioration of lessvital functions including:●local damage which may reduce the durability of thestructure or affect the efficiency of structural or non-structural elements;●unacceptable deformations which affect the efficientuse of structural or non-structural elements, or thefunctioning of equipment;●excessive vibrations which cause discomfort topeople or affect non-structural elements, or thefunctioning of equipment.The ultimate limit state represents the collapse of thestructure, from factors such as: ●loss of equilibrium of the structure or part of thestructure, considered as a rigid body (e.g.overturning or capsizing);●attainment of the maximum resistance capacity ofsections, members or connections by gross yielding,rupture or fracture;●instability of the structure or part thereof, such as bybuckling of columns, plates, shells and stiffenedpanels.The fatigue limit state results from damageaccumulation under the action of cyclic loads and theaccidental limit state is due to accidents such ascollisions or grounding. Overall failure of ship hull girders, which rarelyoccurs, is normally governed by buckling and plasticcollapse of the deck, bottom or sometimes the sideshell stiffened panels. Failure of deck, bottom or sideshell stiffened panels can then lead to progressivecollapse and ultimate hull girder failure. For manyyears, ship structural researchers have been workingtowards the goal of reliability-based limit state designof ship structures. However, reliability-based designrequirescalculation of the ultimate limit state, not onlyof the hull girder, but also of all the structural panelsand other members. Also, these calculations must beperformed a large number of times.Therefore, it is notpractical to use iterative finite element analyses forthese calculations. For efficient computations, ultimatestrength formulations must be developed as closed-form expressions, both for structural components andfor the complete hull girder.A number of studies on the ultimate collapsestrength of ships’ hulls have been undertakentheoretically, numerically and experimentally .Some of the results have been reviewed by the ISSCTechnical Committee III.1 on ‘Ultimate Strength’. Theultimate strength reliability of ships’ hulls,considering existing local damage related to corrosion,fatigue and collision/grounding, has also beenstudied.Previous studies on the development ofa formulation for ultimate hull strength predictionmay be classified into three groups. The first is a linearapproach, where the behaviour of the hull up to failureof the compression flange is assumed to be linearelastic, i.e. ignoring buckling, and the ultimatemoment capacity of the hull is basically expressed asthe ultimate strength of the compression flangemultiplied by the elastic section modulus, witha simple correction for buckling and yielding .The second is an empirical approach, where anexpression is derived on the basis of experimental ornumerical data from scaled or real hull models.The third is an analytical approach, based ona presumed stress distribution over the hull section(plane sections remain plane) from which themoment of resistance of the hull is theoreticallycalculated, taking into account buckling in thecompression flange and yielding in the tensionflange.The first approach is quite simple, but its accuracy isusually wanting because, after buckling of thecompression flange, the behaviour of the hull is nolonger linear, and the neutral axis changes position.Empirical formulations (the second approach) mayprovide reasonable solutions for conventional hulls,but one has to be careful in using empiricalformulations for new and general-type hulls, sincethey are usually derived on the basis of limited data, orfor a particular hull form, using an empiricalformulation . On the other hand, analyticalformulations (the third approach) can be applied tonew or general hulls because they include sectioneffects more precisely.The ship hull ultimate strength formula iseventually expressed as a function of designparameters related to geometric and materialproperties including plate thickness, yield strengthand Young’s modulus. When time-variant structuraldegradation (e.g. corrosion) is considered, the value ofmember thickness at any particular time is a functionof such damage. In probability-based design methods,all the design parameters are treated as the randomvariables. The hull ultimate strength formula forhogging normally differs from that for sagging. Limit state functionMathematically, the limit state function for structuralfailure can be given as a function of the randomvariables, as follows:f （X ）=f （1x ，2x ，………n x ）（1）where f (X) is the limit state function representing themargin between structural capacity and demand (i.e.loads or load effects); x i represents the designparameters.The limit state function f (X) characterizes thecondition of the structure and defines two domains ofsafety with regard to the limit surface (envelope), asfollows:f （X ）>0 in the safe domainf （X ）=0 on the limit surface （2）f （X ）<0 in the unsafe domainWith two independent random variables, theultimate limit state function f (X) for ship hull collapseis usually taken as the margin between ship hullultimate strength u M and total bending moment t M , asfollows:f （X ）=u M -t M （3）where u M and t M are functions of the design variables.Methods of reliability analysisThe methods for structural reliability analysis areusually classified into four types,namely level I, levelII, level III and level IV . The level I methodcorresponds to the deterministic or partial safety factormethod, using only one characteristic mean value foreach variable. A relevant allowable usage factor foreach variable that may be determined by calibration ofa higher level reliability method-based results isapplied in the level I reliability analysis toapproximately supplement the related uncertainties.The level II method uses two values, i.e. mean andstandard deviation, to describe the characteristics ofeach random variable. The reliability index method,e.g. the first-order second-moment method, is a typicalexample of the level II method. The level III method uses the joint probability density function to describethe characteristics of the random variables. Inreliability analyses by the level III method, eitheranalytical approximations (e.g. first- or second-orderreliability methods) or numerical simulations (e.g.Monte Carlo simulation or directional sampling) areapplied. The level IV method compares the integrityand prospect of an object structure with that ofa reference structure through the engineeringeconomic analysis, considering costs and benefitsassociated with construction, consequences ofstructural failure and maintenance/repair. The levelIV method is employed to establish the targetreliabilities.The ship structural reliability analysis is usuallyundertaken by the level III method. Since the theory ofreliability analysis is discussed in many references,e.g. Mansour and Ditlevsen & Madsen , onlya very brief description for the level III method is givenhere. Generally the probability of failure P f can becalculated as follows: f P =⎰≤0f ）（X P （X ）dx （4）where p(X) is the joint probability density function ofthe random variables X=1x ，2x ，………n x associated withloading, material properties, geometric characteristics,etc. andf (X) is the limit state function, defined suchthat negative values imply failure. Since f (X) is usuallya complicated nonlinear function, it is not easy toperform the integration of eq. (4) directly. Therefore,the equation is normally solved by use of approximateprocedures .With these approximations, as indicated in Fig. 2,the limit state surface is usually approximated at thedesign (failure) point by either a tangent hyper-planeor a hyper-parabola, which simplifies the mathematicsrelated to the calculation of the failure probability. Thefirst type of approximation results in the use of aso-called first-order reliability method (FORM) and the second type of approximation is central to theso-called second-order reliability method (SORM).Such methods facilitate the rapid calculation of theprobability of failure, by widely available standardsoftware. Apart from the individual probabilitydistributions of the random variables involved, thecorrelation between them can also be readilyaccounted for in such calculations; all randomvariables for various member types are considereduncorrelated. The result of such a standard reliabilitycalculation is a reliability index β which is related tothe probability of failure f P by:f P =）（βφ-（5） where is the standard normal distribution function.Fig. 2Further considerationsWhile a number of useful methodologies for analyzingthe safety and reliability of ship structures have beendeveloped over the past decades, furtherdevelopments are needed.Some furtherconsiderations in probability-based design of shipstructures are as follows:●geometric parameters may be treated asdeterministic, although this may need to beconfirmed in the case of deck and bottom platingthickness;●elastic modulus may be taken as deterministic, butyield stress needs to be treated as a random variablewith a mean value based on a fuller assessment ofstrain-rate effects on yield in large-scalerepresentative ship-type structures than presentedhere. In the first instance, yield stress values couldbe based on tensile coupon test results when wave-induced bending moments dominate, and similarlyderived static values of yield stress for dominantstill water load conditions.●hull girder and stiffened panel ultimate strengthmodels require benchmarking against realisticmechanical collapse test data so that the distributionparameters for their associated modelling errors canbe evaluated;●when time-variant structural degradation, e.g. dueto corrosion and fatigue, is considered, theprobabilistic characteristics of such damageat any particular time should be quantified. Whilesome work continues in this area, there existprobabilistic corrosion rate estimation models fortanker structures and for bulk carrierstructures;●consensus is required about the preferredmethodology for determining an appropriatereturn period of response for ship designand how this might be achieved given thecurrent status of environmental parameters anddata records;●the load factor methodology promoted in theliterature is extremely promising, particularlybecause its form is compatible with limitstate (LRFD) design code formats. Consensusis required concerning its generality andany further development. Classification Societyand naval experiences should be helpful inidentifying load combinations to be addressed.However, in identifying a safety format, accountshould be taken of relevant ISO codes (e.g. ISO 2394)in this area;●target safety and reliability initially requiresa calibration approach to determine appropriatevalues, followed by adjustments based onjudgments concerning successful designs and targetreliabilities in other industries, whilst recognizingthat floating structures probably need one order ofmagnitude (in probability of failure terms)morereliability than comparable bottom-foundedstructures, and an expectation that component andsystem reliabilities should differ by about one orderin probability of failure terms. In the context oflongitudinally stiffened ships, plate buckling shouldbe treated as a serviceability limit state and not as anultimate limit state;●partial factor determination will require some formof simplified modelling of strength, loading or thereliability process in order that such determinationcan proceed efficiently. Curve- or surface- fitting canbe applied in all cases.船舶结构安全性和可靠性J K帕克[1] P A普莱斯[2]1.韩国釜山国家大学2.英国FAFA工程师顾问摘要最近在研究和发展对该地区船舶结构的设计方法，安全性和可靠性的评估，将阻力、负荷作为因子运用概率的方法来对船体的抗损毁能力进行设计。

船舶设计外文文献翻译

船舶设计外文文献翻译
船舶设计是一门工程学科，它涉及到船舶的结构设计、推进系统、船舶动力学和稳性等方面。

通过船舶设计，可以确保船舶具有良好的航行性能、结构强度和安全性。

船舶设计常常依赖于计算机辅助设计系统，这些系统可以帮助设计师在设计过程中进行模拟和优化。

船舶设计人员需要综合考虑船舶的使用需求、航行条件、载货量和船舶的造价等因素。

他们还需要进行性能预测和结构强度计算，以确保船舶设计满足相关的规范和标准。

现代船舶设计中，船舶外形设计是一个重要的环节。

船舶的外形设计直接影响到其航行性能和稳定性。

通过优化船体外形，可以提高船舶的速度、降低燃油消耗，并减少船舶在恶劣天气和大浪影响下的波浪干扰和倾覆风险。

除了推进系统，船舶的船体结构也是设计的一个重要部分。

船体结构设计需要考虑船舶的载荷分布、结构强度和疲劳寿命等因素。

船体结构的设计要符合相关的规范和标准，以确保船舶的安全性和稳定性。

总之，船舶设计是一门复杂的科学，它需要综合考虑船舶的各个方面和要求。

通过合理的船舶设计，可以确保船舶在各种航行条件下的安全性和性能。

船舶制造中英文对照外文翻译文献

船舶制造中英文对照外文翻译文献(文档含英文原文和中文翻译)Spatial scheduling for large assembly blocks inshipbuildingAbstract: This paper addresses the spatial scheduling problem (SPP) for large assembly blocks, which arises in a shipyard assembly shop. The spatial scheduling problem is to schedule a set of jobs, of which each requires its physical space in a restricted space. This problem is complicated because both the scheduling of assemblies with different due dates and earliest starting times and the spatial allocation of blocks with different sizes and loads must be considered simultaneously. This problem under consideration aims to the minimization of both the makespan and the load balance and includes various real-world constraints, which includes the possible directional rotation of blocks, the existence of symmetric blocks, and the assignment of some blocks to designated workplaces or work teams. The problem is formulated as a mixed integer programming (MIP) model and solved by a commercially available solver. A two-stage heuristic algorithm has been developed to use dispatching priority rules and a diagonal fill space allocation method, which is a modification ofbottom-left-fill space allocation method. The comparison and computational results shows the proposed MIP model accommodates various constraints and the proposed heuristic algorithm solves the spatial schedulingproblems effectively and efficiently.Keywords: Large assembly block; Spatial scheduling; Load balancing; Makespan; Shipbuilding1. IntroductionShipbuilding is a complex production process characterized by heavy and large parts, various equipment, skilled professionals, prolonged lead time, and heterogeneous resource requirements. The shipbuilding process is divided into sub processes in the shipyard, including ship design, cutting and bending operations, block assembly, outfitting, painting, pre-erection and erection. The assembly blocks are called the minor assembly block, the sub assembly block, and the large assembly block according to their size and progresses in the course of assembly processes. This paper focuses on the spatial scheduling problem of large assembly blocks in assembly shops. Fig. 1 shows a snapshot of large assembly blocks in a shipyard assembly shop.Recently, the researchers and practitioners at academia and shipbuilding industries recently got together at “Smart Production Technology Forum in Shipbuilding and Ocean Plant Industries” to recognize that there are various spatial scheduling problems in every aspect of shipbuilding due to the limited space, facilities, equipment, labor and time. The SPPs occur in various working areas such as cutting and blast shops, assembly shops, outfitting shops, pre-erection yard, and dry docks. The SPP at different areas has different requirements and constraints to characterize the unique SPPs. In addition, the depletion of energy resources on land put more emphasis on the ocean development. The shipbuilding industries face the transition of focus from the traditional shipbuilding to ocean plant manufacturing. Therefore, the diversity of assembly blocks, materials, facilities and operations in ship yards increases rapidly.There are some solution pr oviders such as Siemens™ and Dassult Systems™ to provide integrated software including product life management, enterprise resource planning system, simulation and etc. They indicated the needs of efficient algorithms to solve medium- to large-sized SPP problems in 20 min, so that the shop can quickly re-optimize the production plan upon the frequent and unexpected changes in shop floors with the ongoing operations on exiting blocks intact.There are many different applications which require efficient scheduling algorithms with various constraints and characteristics (Kim and Moon, 2003, Kim et al., 2013, Nguyen and Yun, 2014 and Yan et al., 2014). However, the spatial scheduling problem which considers spatial layout and dynamic job scheduling has not been studied extensively. Until now, spatial scheduling has to be carried out by human schedulers only with their experiences and historical data. Even when human experts have much experience in spatial scheduling, it takes a long time and intensive effort to produce a satisfactory schedule, due to the complexity of considering blocks’ geometric shapes, loads, required facilities, etc. In pract ice, spatial scheduling for more than asix-month period is beyond the human schedulers’ capacity. Moreover, the space in the working areas tends to be the most critical resource in shipbuilding. Therefore, the effective management of spatial resources through automation of the spatial scheduling process is a critical issue in the improvement of productivity in shipbuilding plants.A shipyard assembly shop is consisted of pinned workplaces, equipment, and overhang cranes. Due to the heavy weight of large assembly block, overhang cranes are used to access any areas over other objects without any hindrance in the assembly shop. The height of cranes can limit the height of blocks that can be assembled in the shop. The shop can be considered as a two-dimensional space. The blocks are placed on precisely pinned workplaces.Once the block is allocated to a certain area in a workplace, it is desirable not to move the block again to different locations due to the size and weight of the large assembly blocks. Therefore, it is important to allocate the workspace to each block carefully, so that the workspace in an assembly shop can be utilized in a most efficient way. In addition, since each block has its due date which is pre-determined at the stage of ship design, the tardiness of a block assembly can lead to severe delay in the following operations. Therefore, in the spatial scheduling problem for large assembly blocks, the scheduling of assembly processes for blocks and the allocation of blocks to specific locations in workplaces must be considered at the same time. As the terminology suggests, spatial scheduling pursues the optimal spatial layout and the dynamic schedule which can also satisfy traditional scheduling constraints simultaneously. In addition, there are many constraints or requirements which are serious concerns on shop floors and these complicate the SPP. The constraints or requirements this study considered are explained here: (1) Blocks can be put in either directions, horizontal or vertical. (2) Since the ship is symmetric around the centerline, there exist symmetric blocks. These symmetric blocks are required to be put next to each other on the same workplace. (3) Some blocks are required to be put on a certain special area of the workplace, because the work teams on that area has special equipment or skills to achieve a certain level of quality or complete the necessary tasks. (4) Frequently, the production plan may not be implemented as planned, so that frequent modifications in production plans are required to cope with the changes in the shop. At these modifications, it is required to produce a new modified production plan which does not remove or move the pre-existing blocks in the workplace to complete the ongoing operations.(5) If possible at any time, the load balancing over the work teams, i.e., workplaces are desirable in order to keep all task assignments to work teams fair and uniform.Lee, Lee, and Choi (1996) studied a spatial scheduling that considers not only traditional scheduling constraints like resource capacity and due dates, but also dynamic spatial layout of the objects. They usedtwo-dimensional arrangement algorithm developed by Lozano-Perez (1983) to determine the spatial layout of blocks in shipbuilding. Koh, Park, Choi, and Joo (1999) developed a block assembly scheduling system for a shipbuilding company. They proposed a two-phase approach that includes a scheduling phase and a spatial layout phase. Koh, Eom, and Jang (2008) extended their precious works (Koh et al., 1999) by proposing the largest contact area policy to select a better allocation of blocks. Cho, Chung, Park, Park, and Kim (2001) proposed a spatial scheduling system for block painting process in shipbuilding, including block scheduling, four arrangement algorithms and block assignment algorithm. Park et al. (2002) extended Cho et al. (2001) utilizing strategy simulation in two consecutive operations of blasting and painting. Shin, Kwon, and Ryu (2008) proposed a bottom-left-fill heuristic method for spatial planning of block assemblies and suggested a placement algorithm for blocks by differential evolution arrangement algorithm. Liu, Chua, and Wee (2011) proposed a simulation model which enabled multiple priority rules to be compared. Zheng, Jiang, and Chen (2012) proposed a mathematical programming model for spatial scheduling and used several heuristic spatial scheduling strategies (grid searching and genetic algorithm). Zhang and Chen (2012) proposed another mathematical programming model and proposed the agglomeration algorithm.This study presents a novel mixed integer programming (MIP) formulation to consider block rotations, symmetrical blocks, pre-existing blocks, load balancing and allocation of certain blocks to pre-determined workspace. The proposed MIP models were implemented by commercially available software, LINGO® and problems of various sizes are tested. The computational results show that the MIP model is extremely difficult to solve as the size of problems grows. To efficiently solve the problem, a two-stage heuristic algorithm has been proposed.Section 2 describes spatial scheduling problems and assumptions which are used in this study. Section 3 presents a mixed integer programming formulation. In Section 4, a two-stage heuristic algorithm has been proposed, including block dispatching priority rules and a diagonal fill space allocation heuristic method, which is modified from the bottom-left-fill space allocation method. Computational results are provided in Section 5. The conclusions are given in Section 6.2. Problem descriptionsThe ship design decides how to divide the ship into many smaller pieces. The metal sheets are cut, blast, bend and weld to build small blocks. These small blocks are assembled to bigger assembly blocks. During this shipbuilding process, all blocks have their earliest starting times which are determined from the previous operational step and due dates which are required by the next operational step. At each step, the blocks have their own shapes of various sizes and handling requirements. During the assembly, no block can overlap physically with others or overhang the boundary of workplace.The spatial scheduling problem can be defined as a problem to determine the optimal schedule of a given set of blocks and the layout of workplaces by designating the blocks’ workplace simultaneously. As the term implies, spatial scheduling pursues the optimal dynamic spatial layout schedule which can also satisfy traditional scheduling constraints. Dynamic spatial layout schedule can be including the spatial allocation issue, temporal allocation issue and resource allocation issue.An example of spatial scheduling is given in Fig. 2. There are 4 blocks to be allocated and scheduled in a rectangular workplace. Each block is shaded in different patterns. Fig. 2 shows the 6-day spatial schedule of four large blocks on a given workplace. Blocks 1 and 2 are pre-existed or allocated at day 1. The earliest starting times of blocks 3 and 4 are days 2 and 4, respectively. The processing times of blocks 1, 2 and 3 are 4, 2 and 4 days, respectively.The spatial schedule must satisfy the time and space constraints at the same time. There are many objectives in spatial scheduling, including the minimization of makespan, the minimum tardiness, the maximum utilization of spatial and non-spatial resources and etc. The objective in this study is to minimize the makespan and balance the workload over the workspaces.There are many constraints for spatial scheduling problems in shipbuilding, depending on the types of ships built, the operational strategies of the shop, organizational restrictions and etc. Some basic constraints are given as follows; (1) all blocks must be allocated on given workplaces for assembly processes and must not overstep the boundary of the workplace; (2) any block cannot overlap with other blocks; (3) all blocks have their own earliest starting time and due dates; (4) symmetrical blocks needs to be placed side-by-side in the same workspace. Fig. 3 shows how symmetrical blocks need to be assigned; (5) some blocks need to be placed in the designated workspace; (6) there can be existing blocks before the planning horizon; (7) workloads forworkplaces needs to be balanced as much as possible.In addition to the constraints described above, the following assumptions are made.(1) The shape of blocks and workplaces is rectangular.(2 )Once a block is placed in a workplace, it cannot be moved or removed from its location until the process is completed.(3 ) Blocks can be rotated at angles of 0° and 90° (see Fig. 4).(4) The symmetric blocks have the same sizes, are rotated at the same angle and should be placed side-by-side on the same workplace.(5) The non-spatial resources (such as personnel or equipment) are adequate.3. A mixed integer programming modelA MIP model is formulated and given in this section. The objective function is to minimize makespan and the sum of deviation from average workload per workplace, considering the block rotation, the symmetrical blocks, pre-existing blocks, load balancing and the allocation of certain blocks to pre-determined workspace.A workspace with the length LENW and the width WIDW is considered two-dimensional rectangular space. Since the rectangular shapes for the blocks have been assumed, a block can be placed on workspace by determining (x, y) coordinates, where 0 ⩽ x ⩽ LENW and 0 ⩽ y ⩽ WIDW. Hence, the dynamic layout of blocks on workplaces is similar to two-dimensional bin packing problem. In addition to the block allocation, the optimal schedule needs to be considered at the same time in spatial scheduling problems. Z axis is introduced to describe the time dimension. Then, spatial scheduling problem becomes a three-dimensional bin packing problem with various objectives and constraints.The decision variables of spatial scheduling problem are (x, y, z) coordinates of all blocks within athree-dimensional space whose sizes are LENW, WIDW and T in x, y and z axes, where T represents the planning horizon. This space is illustrated in Fig. 5.In Fig. 6, the spatial scheduling of two blocks into a workplace is illustrated as an example. The parameters p1 and p2 indicate the processing times for Blocks 1 and 2, respectively. As shown in z axis, Block 2 is scheduled after Block 1 is completed.4. A two-stage heuristic algorithmThe computational experiments for the MIP model in Section 3 have been conducted using a commercially available solver, LINGO®. Obtaining global optimum solutions is very time consuming, considering the number of variables and constraints. A ship is consisted of more than 8 hundred large blocks and the size of problem using MIP model is beyond today’s computational ability. A two-stage heuristic algorithm has been proposed using the dispatching priority rules and a diagonal fill method.4.1. Stage 1: Load balancing and sequencingPast research on spatial scheduling problems considers various priority rules. Lee et al. (1996) used a priorityrule for the minimum slack time of blocks. Cho et al. (2001) and Park et al. (2002) used the earliest due date. Shin et al. (2008) considered three dispatching priority rules for start date, finish date and geometric characteristics (length, breadth, and area) of blocks. Liu and Teng (1999) compared 9 different dispatching priority rules including first-come first-serve, shortest processing time, least slack, earliest due date, critical ratio, most waiting time multiplied by tonnage, minimal area residue, and random job selection. Zheng et al. (2012) used a dispatching rule of longest processing time and earliest start time.Two priority rules are used in this study to divide all blocks into groups for load balancing and to sequence them considering the due date and earliest starting time. Two priority rules are streamlined to load-balance and sequence the blocks into an algorithm which is illustrated in Fig. 7. The first step of the algorithm in this stage is to group the blocks based on the urgency priority. The urgency priority is calculated by subtracting the earliest starting time and the processing time from the due date for each block. The smaller the urgency priority, the more urgent the block needs to bed scheduled. Then all blocks are grouped into an appropriate number of groups for a reasonable number of levels in urgency priorities. Let g be this discretionary number of groups. There are g groups of blocks based on the urgency of blocks. The number of blocks in each group does not need to be identical.Blocks in each group are re-ordered grouped into as many subgroups as workplaces, considering the workload of blocks such as the weight or welding length. The blocks in each subgroup have the similar urgency and workloads. Then, these blocks in each subgroup are ordered in an ascending order of the earliest starting time. This ordering will be used to block allocations in sequence. The subgroup corresponds to the workplace.If block i must be processed at workplace w and is currently allocated to other workplace or subgroup than w, block i is swapped with a block at the same position of block i in an ascending order of the earliest starting time at its workplace (or subgroup). Since the symmetric blocks must be located on a same workplace, a similar swapping method can be used. One of symmetric blocks which are allocated into different workplace (or subgroups) needs to be selected first. In this study, we selected one of symmetric blocks whichever has shown up earlier in an ascending order of the earliest starting time at their corresponding workplace (or subgroup). Then, the selected block is swapped with a block at the same position of symmetric blocks in an ascending order of the earliest starting time at its workplace (or subgroups).4.2. Stage 2: Spatial allocationOnce the blocks in a workplace (or subgroup) are sequentially ordered in different urgency priority groups, each block can be assigned to workplaces one by one, and allocated to a specific location on a workplace. There has been previous research on heuristic placement methods. The bottom-left (BL) placement method was proposed by Baker, Coffman, and Rivest (1980) and places rectangles sequentially in a bottom-left most position. Jakobs (1996) used a bottom-left method that is combined with a hybrid genetic algorithm (see Fig.8). Liu and Teng (1999) developed an extended bottom-left heuristic which gives priority to downward movement, where the rectangles is only slide leftwards if no downward movement is possible. Chazele (1983) proposed the bottom-left-fill (BLF) method, which searches for lowest bottom-left point, holes at the lowest bottom-left point and then place the rectangle sequentially in that bottom-left position. If the rectangle is not overlapped, the rectangle is placed and the point list is updated to indicate new placement positions. If the rectangle is overlapped, the next point in the point list is selected until the rectangle can be placed without any overlap. Hopper and Turton (2000) made a comparison between the BL and BLF methods. They concluded that the BLF method algorithm achieves better assignment patterns than the BL method for Hopper’s example problems.Spatial allocation in shipbuilding is different from two-dimensional packing problem. Blocks have irregular polygonal shapes in the spatial allocation and blocks continuously appear and disappear since they have their processing times. This frequent placement and removal of blocks makes BLF method less effective in spatial allocation of large assembly block.In order to solve these drawbacks, we have modified the BLF method appropriate to spatial scheduling for large assembly blocks. In a workplace, since the blocks are placed and removed continuously, it is more efficient to consider both the bottom-left and top-right points of placed blocks instead of bottom-left points only. We denote it as diagonal fill placement (see Fig. 9). Since the number of potential placement considerations increases, it takes a bit more time to implement diagonal fill but the computational results shows that it is negligible.The diagonal fill method shows better performances than the BLF method in spatial scheduling problems. When the BLF method is used in spatial allocation, the algorithm makes the allocation of some blocks delayed until the interference by pre-positioned blocks are removed. It generates a less effective and less efficient spatial schedule. The proposed diagonal fill placement method resolve this delays better by allocating the blocks as soon as possible in a greedy way, as shown in Fig. 10. The potential drawbacks from the greedy approaches is resolved by another placement strategy to minimize the possible dead spaces, which will be explained in the following paragraphs.The BLF method only focused on two-dimensional bin packing. Frequent removal and placement of blocks in a workspace may lead to accumulation of dead spaces, which are small and unusable spaces among blocks. A minimal possible-dead space strategy has been used along with the BLF method. Possible-dead spaces are being generated over the spatial scheduling and they have less chance to be allocated for future blocks. The minimal possible-dead space strategy minimizes the potential dead space after allocating the following blocks (Chung, 2001 and Koh et al., 2008) by considering the 0° and 90° rotation of the block and allocating the following block for minimal possible-dead space. Fig. 11 shows an example of three possible-dead space calculations using the neighbor block search method. When a new scheduling block is considered to be allocated, the rectangular boundary of neighboring blocks and the scheduling blocks is searched. This boundary can be calculated by obtaining the smallest and the largest x and y coordinates of neighboring blocks and the scheduling blocks. Through this procedure, the possible-dead space can be calculated as shown in Fig.11. Considering the rotation of the scheduling blocks and the placement consideration points from the diagonal fill placement methods, the scheduling blocks will be finally allocated.In this two-stage algorithm, blocks tend to be placed adjacent to one of the alternative edges and their assignments are done preferentially to minimize fractured spaces.5. Computational resultsTo demonstrate the effectiveness and efficiency of the proposed MIP formulation and heuristic algorithm, the actual data about 800+ large assembly blocks from one of major shipbuilding companies has been obtainedand used. All test problems are generated from this real-world data.All computational experiments have been carried out on a personal computer with a Intel® Core™ i3-2100 CPU @ 3.10 GHz with 2 GB RAM. The MIP model in Section 3 has been programmed and solved using LINGO® version 10.0, a commercially available software which can solve linear and nonlinear models. The proposed two-stage heuristic algorithm has been programmed in JAVA programming language.Because our computational efforts to obtain the optimal solutions for even small problems are more than significant, the complexity of SPP can be recognized as one of most difficult and time consuming problems.Depending on the scaling factor α in objective function of the proposed MIP formulation, the performance of the MIP model varies significantly. Setting α less than 0.01 makes the load balancing capability to be ignored from the optimal solution in the MIP model. For computational experiments in this study, the results with the scaling factor set to 0.01 is shown and discussed. The value needs to be fine-turned to obtain the desirable outcomes.Table 1 shows a comparison of computational results and performance between the MIP models andtwo-stage heuristic algorithm. As shown in Table 1, the proposed two-stage heuristic algorithm finds thenear-optimal solutions for medium and large problems very quickly while the optimal MIP models was not able to solve the problems of medium or large sizes due to the memory shortage on computers. It is observed that the computational times for the MIP problems are rapidly growing as the problem sizes increases. The test problems in Table 1 have 2 workplaces.Table 1.Computational results and performance between the MIP models and two-stage heuristic algorithm.The MIP model Two-stage heuristic algorithmNumber of blocksOptimal solution Time (s) Best known solution Time (s)10 12.360 1014.000 12.360 0.02620 22.380a 38250.000 21.380 0.07830 98.344a 38255.000 30.740 0.21850 ––53.760 0.719100 ––133.780 2.948200 ––328.860 12.523The MIP model Two-stage heuristic algorithmNumber of blocksOptimal solution Time (s) Best known solution Time (s)300 ––416.060 40.154400 ––532.360 73.214Best feasible solution after 10 h in Global Solver of LINGO®.Full-size tableTable optionsView in workspaceDownload as CSVThe optimal solutions for test problems with more than 50 blocks in Table 1 have been not obtained even after 24 h. The best known feasible solutions after 10 h for the test problems with 20 blocks and 30 blocks are reported in Table 1. It is observed that the LINGO® does not solve the nonlinear constraints very well as shown in Table 1. For very small problem with 10 blocks, the LINGO® was able to achieve the optimal solutions. For slightly bigger problems, the LINGO® took significantly more time to find feasible solutions. From this observation, the approaches to obtain the lower bound through the relaxation method and upper bounds are significant required in future research.In contrary, the proposed two-stage heuristic algorithm was able to find the good solutions very quickly. For the smallest test problem with 10 blocks, it was able to find the optimal solution as well. The computational times are 1014 and 0.026 s, respectively, for the MIP approach and the proposed algorithm. Interestingly, the proposed heuristic algorithm found significantly better solutions in only 0.078 and 0.218 s, respectively, for the test problems with 20 and 30 blocks. For these two problems, the LINGO® generates the worse solutions than the heuristics after 10 h of computational times. The symbol ‘–’ in Table 1 indicates that the Global Solver of LINGO® did not find the feasible solutions.Another observation on the two-stage heuristic algorithms is the robust computational times. The computation times does not change much as the problem sizes increase. It is because the simple priority rules are used without considering many combinatorial configurations.Fig. 12 shows partial solutions of test problems with 20 and 30 blocks on 2 workplaces. The purpose of Fig. 12 is to show the progress of production planning generated by the two-stage heuristic algorithm. Two workplaces are in different sizes of (40, 30) and (35, 40), respectively.6. ConclusionsAs global warming is expected to open a new way to transport among continent through North Pole Sea and to expedite the oceans more aggressively, the needs for more ships and ocean plants are forthcoming. The shipbuilding industries currently face increased diversity of assembly blocks in limited production shipyard. Spatial scheduling for large assembly blocks holds the key role in successful operations of the shipbuilding。

船舶设计外文文献翻译

外文文献翻译（译成中文1000字左右）：【主要阅读文献不少于5篇，译文后附注文献信息，包括：作者、书名（或论文题目）、出版社（或刊物名称）、出版时间（或刊号）、页码。

提供所译外文资料附件（印刷类含封面、封底、目录、翻译部分的复印件等，网站类的请附网址及原文】Ships Typed According to Means of Physical SupportThe mode of physical support by which vessels can be categorized assumes that the vessel is operating under designed conditions. Ships are designed to operate above, on, or below the surface of the sea, so the air-sea interface will be used as the reference datum. Because the nature of the physical environment is quite different for the three regions just mentioned, the physical characteristics of ships designed to operate in those regions can be diverse.Aerostatic SupportThere are two categories of vessels that are supported above the surface of the sea on a self-induced cushion of air. These relatively lightweight vehicles are capable of high speeds, since air resistance is considerably less than water resistance, and the absence of contact with small waves combined with flexible seals reduces the effects of wave impact at high speed. Such vessels depend on lift fans to create a cushion oflow-pressure air in an underbody chamber. This cushion of air must be sufficient to support the weight of the vehicle above the water surface.The first type of vessel has flexible “skirts” that entirely surround the air cushion and enable the ship to rise completely above the sea surface. This is called an air cushion vehicle (ACV), and in a limited sense it is amphibious.The other type of air-cushion craft has rigid side walls or thin hulls that extend below the surface of the water to reduce the amount of air flow required to maintain the cushion pressure. This type is called a captured-air-bubble vehicle (CAB). It requires less lift-fan power than an ACV, is more directionally stable, and can be propelled by water jets or supercavitating propellers. It is not amphibious, however, and has not yet achieved the popularity of the ACVs, which include passenger ferries, cross-channel automobile ferries, polar-exploration craft, landing craft, and riverine warface vessels. Hydrodynamic SupportThere are also two types of vessels that depend on dynamic support generated by relatively rapid forward motion of specially designed hydrodynamic shapes either on or beneath the surface of the water. A principle of physics states that any moving object that can produce an unsymmetrical flow pattern generates a lift force perpendicular to the direction of motion. Just as an airplane with (airfoil) produces lift when moving through the air, a hydrofoil, located beneath the surface and attached bymeans of a surface piercing strut, can dynamically support a vessel’s hull above the water.Planning hulls are hull forms characterized by relatively flat bottoms and shallowV-sections (especially forward of amidships) that produce partial to nearly full dynamic support for light displacement vessels and small craft at higher speeds. Planning craft are generally restricted in size and displacement because of the required power-to-weight ratio and the structural stresses associated with traveling at high speed in waves. Most planning craft are also restricted to operations in reasonably clam water, although some “deep V” hull forms are capable of operation in rough water.Hydrostatic SupportFinally, there is the oldest and most reliable type of support, hydrostatic support. All ships, boats, and primitive watercraft up to the twentieth century have depended upon the easily attained buoyant force of water for their operation.This hydrostatic support, commonly recognized as flotation, can be explained by a fundamental physical law that the ancient philosopher-mathematician Archimedes defined in the second century B.C. Archimedes’ Principle states that a body immersed in a liquid is buoyed up (or acted upon) by a force equal to the weight of the liquid displaced. This principle applies to all vessels that float (or submerge) in water---salt or fresh. And from this statement the name of the ships in the category are derived; they are generally called displacement hulls.Although this ship type is very familiar, its subcategories warrant special discussion. For example, in some vessels reasonably high speed must be combined with the ability to carry light cargo or to move more comfortably in rough water than a planning hull. High-speed planning-hull characteristics can be modified to produce a semidisplacement hull or semiplaning hull. These compromise craft, of course not as fast as full-planing hulls but faster than conventional displacement hull, must have more power and less weight than the latter. Such types are obviously the result of “tradeoffs.”The example cited above lies between clear-cut physically defined categories----it is not a good example of a variation of a true displacement-type ship. The latter must be recognized primarily as a displacement vessel, and its variations depend primarily on the distribution of buoyant volume----the extent of the depth and breadth of the hull below the water.The most ubiquitous type of displacement ship can be generally classified as the common carrier, a seagoing vessel. It may be employed for passenger service, light cargo-carrying, fishing by trawling or for hundreds of other tasks that do not require exceptional capacity, speed, submergence, or other special performance. It is the most common and easily recognizable type of ship, with moderate displacement, moderate speeds, moderate to large lengths, and moderate capacities. It usually embodies the maximum in cruising range and seaworthiness. It is the “ship for all seasons.” It is the standard to which all other ship classifications in the displacement category may be referred.The closest relative to this standard vessel, which plays a crucial role not only in world commerce but in the survival of the industrial world as well, is the bulk, oil carrier, the tanker, or supertanker. These terminologies are common but unspecific, and in this discussion they are inadequate, for what was called a supertanker several years ago is today not a supertanker. The industry itself has created a far more explicit nomenclature. Based upon the index of 1000000 tons oil cargo capacity, the size categories are LCC (large crude carrier), VLCC (very large crude carrier), and ULCC (ultra large crude carrier). Any tanker greater than 100000 tons but less than 200000 is a LCC, those between 200000 and 400000 are VLCCs, and those over 400000 are ULCCs. The current necessity for these designations becomes clear when we realize that before 1956 there were no tankers larger than 50000 tons, and not until the early sixties were any ships built larger than 100000 tons. In 1968 the first ship over 300000 tons was built. With their bulk and enormous capacity (four football fields can be placed end to end on one of their decks), these ships are designed and built to be profit-makers, enormously long, wide, and deep, carrying thousands of tons of crude oil per voyage at the least cost. Few of these elephantine tankers have more than one propeller shaft of rudder. Their navigation bridges are nearly one quarter of a mile from their bows. Their top service speed is so low that a voyage from an Arabian oil port to a European destination normally takes two months.Such vessels belong to a category of displacement ship that has a great range of buoyant support. They have a very large and disproportionate hull volume below the surface when fully loaded. Indeed, the cargo weight far exceeds the weight of the ship itself. The draft or depth of water required for a fully loaded VLCC runs to 50 or 60 feet and the ULCC may be 80 feet. Such ships belong in the exclusive category of displacement vessels called deep displacement ships.There exists another type of displacement hull with extreme draft. However, it is similarity to the crude-oil carrier of the preceding discussion goes no further than that. This type of vessel is called the SWATH( small waterplane area twin hull). Briefly, this rather rare breed of ship is designed for relatively high speed and stable platform in moderately rough water. Its future is problematical, but the theory of placing the bulk of the displacement well below the surface and extending the support to the above-water platform or deck through the narrow waterline fins or struts is sound. Twin hulls connected by an upper platform provide the necessary operating stability. The most significant class of displacement hull for special application is the sub marine, a vessel for completely submerged operation. The nature of the submarine and a description of her various operational attitudes, both static and dynamic, is covered in subsequent chapters. It is only necessary here to emphasize that submerisible vessels are specifically displacement vessels applying the theory of Archimedes’ Principle and all that it implies.Multihull VesselsThere is one other type of hull in common use that has not yet been mentioned, primarily because it fits into none of the categories described but rather can exist comfortably in any. This craft is the so-called multihull vessel----the catamaran andthe trimaran. These vessels are most frequently displacement hulls in their larger sizes, such as the SWATH mentioned above, or more conventionally, ocean research vessels requiring stable platforms and protected areas for launching equipment. There are also the twin-hulled CAB vessels mentioned earlier and high-speed planning catamarans. Actually, the multihull ship is an adaptation of any of the basic hull categories to a special application that requires exceptional transverse stability and/ or the interhull working area.中文翻译：按照物理支撑方式而划分的船舶类型就船舶分类而言，物理支撑形式是基本于船舶在设计情况下进行的假定。

船舶稳性文献翻译-魏豪

在船舶受损的情况下，通过可使用的措施来改善船舶稳性与安全：机遇与挑战摘要：船舶设计通常需要确保船舶的稳性的角度来确保船舶的安全。

然而，船舶的整体安全，只能通过整体、平衡、经济的方式来将被动的设计和主动的措施结合来确保。

从船舶稳性的角度来说，因此需要时间来系统地考虑为船舶安全提供被认可的，符合规范的一些可行的措施。

就这个方面而言，本文主要是阐明常见的挑战，激发在船舶受损情况下，可行的措施的讨论，并为之提供一定的思路。

文章的目的是为执行可靠的船舶稳性安全的措施提供基础，这些措施有助于做出有效、稳固的、可行的风险控制选择。

1、引言对于破损船舶稳性所要求的安全级别，通常是在船舶设计阶段，有适当的固定的措施来进行考量和有效性的评估，这些措施一般都是不符合适用的相关规则、规定。

这些措施是作为潜在的设计选项（船体形状，分隔，系统冗余和可行性等等），以及为了船舶装载情况的考虑。

过去的30年，一致的研究和发展已经促使国际海事组织和机构研究船舶进水过程的理论解释，并且关注于适用于所有船型的新的船舶破损稳性的概率理论，新船型设计的延伸和被称为挑战的设计局限，以及安全回港规则（safe return to port）。

最近相关的研究和发展包括，由欧盟委员会赞助的SAFEDOR（Breinholt. 2009）研究项目的进展，评估客船的破损稳性和不沉的概率方法的发展与改进，GOALDS项目（Papanikolaou. 2013）,在为滚装船和大型豪华游轮建立一个合理的、目标为导向的合理的破损稳性规则框架方面的进展。

在这个过程中，风险为基础和经济的考虑已经得到了大量地运用。

主要的发现就是，船舶的整体安全只能靠，以整体、平衡、经济的方式将固定的设计方式和灵活的操作方法结合起来的方式实现。

进水时间和疏散时间以及它们如何关联是确定船舶安全和进水的安全阈值的基础概念。

相关研究已经有人做了（Jasionowski.2011），他在解决，在破损滚装船的车辆甲板上发生大范围的进水时，给乘客所留下的绝对时间的复杂问题的过程中进行了一定的研究。

船舶设计论文中英文外文翻译文献

船舶设计论文中英文外文翻译文献XXX shipbuilding。

with a single large container vessel consisting of approximately 1.5 n atomic components in a n hierarchy。

this n is considered a XXX involves a distributed multi-agent n that runs on top of PVM.2 XXXShip XXX process。

as well as the final product's performance and safety。

nal design XXX-consuming and often fail to consider all the complex factors XXX。

there is a need for a more XXX designers.3 The Role of HPCN in Ship Design nHPCN。

or high-performance computing and orking。

has the potential to XXX utilizing the massive parallel processing power of HPCN。

designers XXX changes。

cing the time and cost of thedesign process。

nally。

HPCN can handle the complex XXX。

XXX.4 XXX XXX of the HPCN n Support ToolThe XXX ship designers is implemented as a distributed multi-agent n that runs on top of PVM。

介绍船舶英语作文带翻译

介绍船舶英语作文带翻译英文：Boats and ships have always been a source offascination for me. Whether it's a small fishing boat or a massive cruise ship, there's something about being out on the water that I find incredibly peaceful and exciting at the same time.As a child, I used to spend hours building model boats and ships, dreaming of the day when I could actually go out on the water in one. When I finally got the chance to go on a sailing trip with some friends, it was everything I had hoped for and more. The feeling of the wind in my hair, the sound of the waves against the hull, and the sense of freedom that comes with being out on the open sea is something that I will never forget.Over the years, I've learned a lot about boats and ships, from the different types and sizes to theterminology used by sailors. For example, I now know that a "bow" is the front of the ship, while the "stern" is the back. I've also learned about the importance of maintaining a ship and the various tasks that need to be done to keepit in good working order.One of the most interesting things I've learned is the different ways that boats and ships are used around the world. From fishing boats in the Mediterranean to cargo ships crossing the Pacific, each type of vessel has its own unique purpose and challenges.中文：船舶一直是我着迷的对象。

(完整版)船舶专业英语(课文+翻译)

Chapter 1 Ship Design（船舶设计）Lesson 2 Ships Categorized（船舶分类）2.1 Introduction（介绍）The forms a ship can take are innumerable. 一艘船能采用的外形是不可胜数的A vessel might appear to be a sleek seagoing hotel carrying passengers along to some exotic destination; a floating fortress bristling with missile launchers; 。

or an elongated box transporting tanks of crude oil and topped with complex pipe connections. 一艘船可以看做是将乘客一直运送到外国目的地的优美的远航宾馆。

竖立有导弹发射架的水面堡垒及甲板上铺盖有复杂管系的加长罐装原油运输轮None of these descriptions of external appearance, however, does justice to the ship system as a whole and integrated unit所有这些外部特点的描述都不能说明船舶系统是一个总的集合体self-sufficient,seaworthy, and adequately stable in its function as a secure habitat for crew and cargo. ——船员和货物的安全性功能：自给自足，适航，足够稳定。

This is the concept that the naval architect keeps in mind when designing the ship and that provides the basis for subsequent discussions, not only in this chapter but throughout the entire book.这是一个造船工程师设计船舶使必须记住的、能为以后讨论提供根据的观念，不仅涉及本章也贯穿全书。

船舶专业英语(课文+翻译)

Chapter 1 Ship Design（船舶设计）Lesson 2 Ships Categorized（船舶分类）Introduction（介绍）The forms a ship can take are innumerable. 一艘船能采用的外形是不可胜数的A vessel might appear to be a sleek seagoing hotel carrying passengers along to some exotic destination; a floating fortress bristling with missile launchers; 。

or an elongated box transporting tanks of crude oil and topped with complex pipe connections. 一艘船可以看做是将乘客一直运送到外国目的地的优美的远航宾馆。

竖立有导弹发射架的水面堡垒及甲板上铺盖有复杂管系的加长罐装原油运输轮None of these descriptions of external appearance, however, does justice to the ship system as a whole and integrated unit所有这些外部特点的描述都不能说明船舶系统是一个总的集合体—self-sufficient,seaworthy, and adequately stable in its function as a secure habitat for crew and cargo. ——船员和货物的安全性功能：自给自足，适航，足够稳定。

有关船舶的文章英文版范文

有关船舶的文章英文版范文The Majesty and Complexity of Maritime Vessels.The vast ocean, a mysterious and unexplored frontier, has always fascinated humanity. Since the dawn of civilization, mankind has dreamed of conquering the waves and traversing the vast blue. This dream has been realized through the creation and evolution of maritime vessels, which have played a pivotal role in shaping the world we know today.Ships, the epitome of maritime transportation, have a rich and diverse history. From the humble wooden dhows that plied the trade routes of the ancient world to the massive container ships that traverse the globe today, thesevessels have evolved to meet the changing needs of humanity. Each ship, whether a cargo carrier, a passenger liner, or a naval warship, is a testament to the ingenuity and craftsmanship of its makers.The design and construction of a ship are intricate and complex processes. The hull, the very backbone of the vessel, must be sturdy and resilient, able to withstand the rigors of the ocean. Materials such as steel and aluminum are used to create a strong yet lightweight structure, while advanced technologies like hydrodynamics and computer-aided design ensure that the ship's shape is optimized for speed and efficiency.The propulsion system.。

船舶设计论文中英文外文翻译文献

中英文外文翻译文献Ship Design OptimizationThis contribution is devoted to exploiting the analogy between a modern manufacturing plant and a heterogeneous parallel computer to construct a HPCN decision support tool for ship designers. The application is a HPCN one because of the scale of shipbuilding - a large container vessel is constructed by assembling about 1.5 million atomic components in a production hierarchy. The role of the decision support tool is to rapidly evaluate the manufacturing consequences of design changes. The implementation as a distributed multi-agent application running on top of PVM is described1 Analogies between Manufacturing and HPCNThere are a number of analogies between the manufacture of complex products such as ships, aircraft and cars and the execution of a parallel program. The manufacture of a ship is carried out according to a production plan which ensures that all the components come together at the right time at the right place. A parallel computer application should ensure that the appropriate data is available on the appropriate processor in a timely fashion.It is not surprising, therefore, that manufacturing is plagued by indeterminacy exactly as are parallel programs executing on multi-processor hardware. This has caused a number of researchers in production engineering to seek inspiration in otherareas where managing complexity and unpredictability is important. A number of new paradigms, such as Holonic Manufacturing and Fractal Factories have emerged [1,2] which contain ideas rather reminiscent of those to be found in the field of Multi- Agent Systems [3, 4].Manufacturing tasks are analogous to operations carried out on data, within the context of planning, scheduling and control. Also, complex products are assembled at physically distributed workshops or production facilities, so the components must be transported between them. This is analogous to communication of data between processors in a parallel computer, which thus also makes clear the analogy between workshops and processors.The remainder of this paper reports an attempt to exploit this analogy to build a parallel application for optimizing ship design with regard to manufacturing issues.2 Shipbuilding at Odense Steel ShipyardOdense Steel Shipyard is situated in the town of Munkebo on the island of Funen. It is recognized as being one of the most modern and highly automated in the world. It specializes in building VLCC's (supertankers) and very large container ships. The yard was the first in the world to build a double hulled supertanker and is currently building an order of 15 of the largest container ships ever built for the Maersk line. These container ships are about 340 metres long and can carry about 7000 containers at a top speed of 28 knots with a crew of 12.Odense Steel Shipyard is more like a ship factory than a traditional shipyard. The ship design is broken down into manufacturing modules which are assembled and processed in a number of workshops devoted to, for example, cutting, welding and surface treatment. At any one time, up to 3 identical ships are being built and a new ship is launched about every 100 days.The yard survives in the very competitive world of shipbuilding by extensive application of information technology and robots, so there are currently about 40 robots at the yard engaged in various production activities. The yard has a commitment to research as well, so that there are about 10 industrial Ph.D. students working there, who are enrolled at various engineering schools in Denmark.3 Tomorrow's Manufacturing SystemsThe penetration of Information Technology into our lives will also have its effect in manufacturing industry. For example, the Internet is expected to become thedominant trading medium for goods. This means that the customer can come into direct digital contact with the manufacturer.The direct digital contact with customers will enable them to participate in the design process so that they get a product over which they have some influence. The element of unpredictability introduced by taking into account customer desires increases the need for flexibility in the manufacturing process, especially in the light of the tendency towards globalization of production. Intelligent robot systems, such as AMROSE, rely on the digital CAD model as the primary source of information about the work piece and the work cell [5,6].This information is used to construct task performing, collision avoiding trajectories for the robots, which because of the high precision of the shipbuilding process, can be corrected for small deviations of the actual world from the virtual one using very simple sensor systems. The trajectories are generated by numerically solving the constrained equations of motion for a model of the robot moving in an artificial force field designed to attract the tool centre to the goal and repell it from obstacles, such as the work piece and parts of itself. Finally, there are limits to what one can get a robot to do, so the actual manufacturing will be performed as a collaboration between human and mechatronic agents.Most industrial products, such as the windmill housing component shown in Fig. 1, are designed electronically in a variety of CAD systems.Fig. 1. Showing the CAD model for the housing of a windmill. The model, made using Bentley Microstation, includes both the work-piece and task-curve geometries.4 Today's Manufacturing SystemsThe above scenario should be compared to today's realities enforced by traditional production engineering philosophy based on the ideas of mass production introduced about 100 years ago by Henry Ford. A typical production line has the same structure as a serial computer program, so that the whole process is driven by production requirements. This rigidity is reflected on the types of top-down planning and control systems used in manufacturing industry, which are badly suited to both complexity and unpredictability.In fact, the manufacturing environment has always been characterized by unpredictability. Today's manufacturing systems are based on idealized models where unpredictability is not taken into account but handled using complex and expensive logistics and buffering systems.Manufacturers are also becoming aware that one of the results of the top-down serial approach is an alienation of human workers. For example, some of the car manufacturers have experimented with having teams of human workers responsible for a particular car rather than performing repetitive operations in a production line. This model in fact better reflects the concurrency of the manufacturing process than the assembly line.5 A Decision Support Tool for Ship Design OptimizationLarge ships are, together with aircraft, some of the most complex things ever built. A container ship consists of about 1.5 million atomic components which are assembled in a hierarchy of increasingly complex components. Thus any support tool for the manufacturing process can be expected to be a large HPCN application.Ships are designed with both functionality and ease of construction in mind, as well as issues such as economy, safety, insurance issues, maintenance and even decommissioning. Once a functional design is in place, a stepwise decomposition of the overall design into a hierarchy of manufacturing components is performed. The manufacturing process then starts with the individual basic building blocks such as steel plates and pipes. These building blocks are put together into ever more complex structures and finally assembled in the dock to form the finished ship.Thus a very useful thing to know as soon as possible after design time are the manufacturing consequences of design decisions. This includes issues such as whether the intermediate structures can actually be built by the available production facilities, the implications on the use of material and whether or not the production can be efficiently scheduled [7].Fig.2. shows schematically how a redesign decision at a point in time during construction implies future costs, only some of which are known at the time. Thus a decision support tool is required to give better estimates of the implied costs as early as possible in the process.Simulation, both of the feasibility of the manufacturing tasks and the efficiency with which these tasks can be performed using the available equipment, is a very compute-intense application of simulation and optimization. In the next section, we describe how a decision support tool can be designed and implemented as a parallel application by modeling the main actors in the process as agents.Fig.2. Economic consequences of design decisions. A design decision implies a future commitment of economic resources which is only partially known at design time.6 Multi-Agent SystemsThe notion of a software agent, a sort of autonomous, dynamic generalization of an object (in the sense of Object Orientation) is probably unfamiliar to the typical HPCN reader in the area of scientific computation. An agent possesses its own beliefs, desires and intentions and is able to reason about and act on its perception of other agents and the environment.A multi-agent system is a collection of agents which try to cooperate to solve some problem, typically in the areas of control and optimization. A good example is the process of learning to drive a car in traffic. Each driver is an autonomous agent which observes and reasons about the intentions of other drivers. Agents are in fact a very useful tool for modeling a wide range of dynamical processes in the real world, such as the motion of protein molecules [8] or multi-link robots [9]. For other applications, see [4].One of the interesting properties of multi-agent systems is the way global behavior of the system emerges from the individual interactions of the agents [10]. The notion of emergence can be thought of as generalizing the concept of evolution in dynamical systems.Examples of agents present in the system are the assembly network generator agent which encapsulates knowledge about shipbuilding production methods for planning assembly sequences, the robot motion verification agent, which is a simulator capable of generating collision-free trajectories for robots carrying out their tasks, the quantity surveyor agent which possesses knowledge about various costs involved in the manufacturing process and the scheduling agent which designs a schedule for performing the manufacturing tasks using the production resources available.7 Parallel ImplementationThe decision support tool which implements all these agents is a piece of Object- Oriented software targeted at a multi-processor system, in this case, a network of Silicon Graphics workstations in the Design Department at Odense Steel Shipyard. Rather than hand-code all the communication between agents and meta-code for load balancing the parallel application, abstract interaction mechanisms were developed. These mechanisms are based on a task distribution agent being present on each processor. The society of task distribution agents is responsible for all aspects of communication and migration of tasks in the system.The overall agent system runs on top of PVM and achieves good speedup andload balancing. To give some idea of the size of the shipbuilding application, it takes 7 hours to evaluate a single design on 25 SGI workstations.From：Applied Parallel Computing Large Scale Scientific and Industrial Problems Lecture Notes in Computer Science, 1998, Volume 1541/1998, 476-482, DOI: 10.1007/BFb0095371 .中文翻译：船舶设计优化这一贡献致力于开拓类比现代先进制造工厂和一个异构并行计算机,构建了一种HPCN 决策支援工具给船舶设计师。

船舶论文外文翻译

1.1Container Shipping ChangesAs commerce has become and continues to be more international, ocean container shipments have grown exponentially as a means of moving most any kind of freight from one port to another. Buffered by waves of change touching other modes of transport, ocean carriers are in a constant process of altering the way they conduct their business to meet current needs of shipping customers. While chartered to serve a wider public with insight about the industry, the Container Shipping Information Service (CSIS) is able to provide a spokesperson from one of its 24 member companies to treat objectively with commonly shared issues. Andres Kulka, senior vice president of CSAV Group North America shares just such insights.In an environment of high transportation costs, ocean container shipping‟s mix of speed, cost, availability and capability offers a superior value proposition, especially as logistics and supply chain management processes and systems are implemented by a growing range of shippers. Because of their shelf life or time value certain commodities must be transported by air. Increases in the need to speedily transport these commodities along with the greater economy will be a primary factor for airfreight growth in the future. But spiraling fuel surcharges and resulting cost consciousness among shippers opens opportunities for ocean carriers to gain market share in the broader spectrum of non- perishable commodities where airfreight‟s cost effectiveness has diminished.Shortages of containers is produced by commercial imbalance situations. When exports outgrow imports in a geographic region, you may face equipment shortages, as was the case in Asia. When you add imbalance by type of equipment to the situation, the situation worsens. While at present leasing containers are available to meet the demand in Asia, container pricing has reached levels of $2,500 for a dry,due largely, to the increase of commodities costs and deterioration of the US exchangeThere have been reports of shortages of containers, particularly for cargo moving from Asia.Under these conditions,shipping lines are relying primarily on empty repositioning to Asia rather than use of fresh equipment.The shortage of equipment in the US today is due to two primary factors. First,exports are growing at high rates, mainly because of devaluation of the USdollar.Additionally imports are pretty much staggered causing, again, a commercial imbalance. Secondly, last year many nonprofitable international intermodal lanes were eliminated. This reduced the stock of containers at some inland locations available for exports.Location specific equipment shortages have created the need for increasing empty container repositioning. That is one of the reasons export freight rates have gone up. Media pays great attention to Asian business, but how healthy is container shipping in other regions, say Latin America?In fact trade with Latin America has been sensitive to the sharp fall of theUS dollar. For example in 2007 the Brazilian real was down 17% and the Chilean peso fell 7%. For exports total 2007 volumes for Latin America were about 800,000 TEU (twenty-foot equivalent units), approximately 20% greater than 2006. Top commodities exported to Latin America have been resins,chemicals, plastics, forest products and general merchandise. Higher rates have followed the increase in export demand.Foodstuffs and forest products dominate import volumes from South America, about 970,000 TEU in 2007. Unlike exports, import volume growth—5.5% greater than 2006—has slowed due to the decline of the US dollar. Import rates have risen, but not nearly as strongly as export rates. So far in 2008 the US dollar has continued its downward trend. We are very cautious about the future outlook. Even though exports will probably continue growing at high rates, imports might continue decreasing.1.2Discussion of Structural Standards DevelopmentTaken as a whole, there has been a piecemeal approach to structural design standards. As technical developments occur (models of various structural behaviours, risk methodologies), they have been incorporated into structural standards. Individuals and rule committees have framed their own rules with an emphasis on certain load/strength/failure models, coupled with some risk avoidance strategy (explicit or implicit). It is hardly surprising that various standards are different, even quite different. More,rather than fewer, concepts are available to those who develop structural standards. In the absence of a binding philosophy of structural behaviour, there will continue to be divergence along the way to improved standards. It must be appreciated that all current standards “work”. Any of the current naval and commercial ship design approaches can be used to produce structural designs that function with adequate reliability over a 20+ yearlife expectancy, unless subjected to poor maintenance, human operational error, or deliberate damage. Changes to standards are, therefore, resisted by all those who have invested time and effort in them as developers and users. The rationale for change must be presented well, and its benefits have to outweigh its costs.Experienced designers recognize that structural behaviour can be very complex. Despite this, it is necessary to use simple, practical approaches in design standards, to avoid adding to the problem through overly-complex rules that are difficult to apply and more so to check and audit. Stress is the primary load-effect that standards focus on, partly because it is so readily calculated. The main concerns are material yielding, buckling and fatigue. All of these are local behaviours, and all are used as surrogates for actual structural failure. A structure is a system, comprised of elements, which in turn are built from materials.As an example, yielding can be considered. Yielding is a material level …failure‟, very common, usually very localized, and usually producing noobservable effect. It can be quite irrelevant. The important issue is the behaviour and failure of the structural system, even at the level of the structural components. Ship structures are especially redundant structures, quite unlike most civil structures and buildings. Ship structures are exposed to some of the harshest loading regimes, yet are usually capable of tolerating extensive material and component failure, prior to actual structural collapse.An essential deficiency of all traditional structural standards has been the failure to consider the structural redundancy (path to failure) and identify weaknesses in the system. Areas of weakness are normally defined as those parts that will first yield or fail.However, far more important is the ability of the structure to withstand these and subsequent local/material failures and redistribute the load. The real weaknesses are a lack of secondary load paths. It is often assumed, wrongly, that initial strength is a valid indicator for ultimate strength, and far simpler to assess. There is a need to focus on ways of creating robust structures, much as we use subdivision to create adequate damage stability. As another example, consider frames under lateral loads. When designed properly, frames can exhibit not only sufficient initial strength, but substantial reserve strength, due to the secondary load path created by axial stresses in the plate and frame. In effect, it is possible to create a ductile structure (analogous to a ductile material). If we instead use current designstandards that emphasize elastic section modulus, we risk creating a …brittle‟ structure, even w hen built from ductile materials.In the case of fatigue and buckling, it is again necessary to stand back from consideration of the initial effects, and examine whether there is sufficient reserve (secondary load paths). When there is no such reserve, there is the structural equivalent of a subdivision plan that cannot tolerate even one compartment flooding.The above discussion talks only about structural response, and indicated some gaps. Similar gaps exist in our knowledge of loads. The complexity of ship structures, the complexity of the loads that arise in a marine environment, and the dominating influence of human factors in any risk assessment for vessels, all present daunting challenges.The project team‟s approach to this project, described in the following sections, has intended to provide part of the basis for future design standard development.1.1集装箱运输的变化当商业已成为并将继续更加国际化，远洋集装箱运输已成为成倍增长的将任何种类的货物从一个港口移到另一个港口的手段。

船舶论文-外文翻译

1.1Container Shipping ChangesAs commerce has become and continues to be more international, ocean container shipments have grown exponentially as a means of moving most any kind of freight from one port to another. Buffered by waves of change touching other modes of transport, ocean carriers are in a constant process of altering the way they conduct their business to meet current needs of shipping customers. While chartered to serve a wider public with insight about the industry, the Container Shipping Information Service (CSIS) is able to provide a spokesperson from one of its 24 member companies to treat objectively with commonly shared issues. Andres Kulka, senior vice president of CSAV Group North America shares just such insights.In an environment of high transportation costs, ocean container shipping’s mix of speed, cost, availability and capability offers a superior value proposition, especially as logistics and supply chain management processes and systems are implemented by a growing range of shippers. Because of their shelf life or time value certain commodities must be transported by air. Increases in the need to speedily transport these commodities along with the greater economy will be a primary factor for airfreight growth in the future. But spiraling fuel surcharges and resulting cost consciousness among shippers opens opportunities for ocean carriers to gain market share in the broader spectrum of non- perishable commodities where airfreight’s cost effectiveness has diminished.Shortages of containers is produced by commercial imbalance situations. When exports outgrow imports in a geographic region, you may face equipment shortages, as was the case in Asia. When you add imbalance by type of equipment to the situation, the situation worsens. While at present leasing containers are available to meet the demand in Asia, container pricing has reached levels of $2,500 for a dry,due largely, to the increase of commodities costs and deterioration of the US exchangeThere have been reports of shortages of containers, particularly for cargo moving from Asia.Under these conditions,shipping lines are relying primarily on empty repositioning to Asia rather than use of fresh equipment.The shortage of equipment in the US today is due to two primary factors. First,exports are growing at high rates, mainly because of devaluation of the USdollar.Additionally imports are pretty much staggered causing, again, a commercial imbalance. Secondly, last year many nonprofitable international intermodal lanes were eliminated. This reduced the stock of containers at some inland locations available for exports.Location specific equipment shortages have created the need for increasing empty container repositioning. That is one of the reasons export freight rates have gone up. Media pays great attention to Asian business, but how healthy is container shipping in other regions, say Latin America?In fact trade with Latin America has been sensitive to the sharp fall of theUS dollar. For example in 2007 the Brazilian real was down 17% and the Chilean peso fell 7%. For exports total 2007 volumes for Latin America were about 800,000 TEU (twenty-foot equivalent units), approximately 20% greater than 2006. Top commodities exported to Latin America have been resins,chemicals, plastics, forest products and general merchandise. Higher rates have followed the increase in export demand.Foodstuffs and forest products dominate import volumes from South America, about 970,000 TEU in 2007. Unlike exports, import volume growth—5.5% greater than 2006—has slowed due to the decline of the US dollar. Import rates have risen, but not nearly as strongly as export rates. So far in 2008 the US dollar has continued its downward trend. We are very cautious about the future outlook. Even though exports will probably continue growing at high rates, imports might continue decreasing.1.2Discussion of Structural Standards DevelopmentTaken as a whole, there has been a piecemeal approach to structural design standards. As technical developments occur (models of various structural behaviours, risk methodologies), they have been incorporated into structural standards. Individuals and rule committees have framed their own rules with an emphasis on certain load/strength/failure models, coupled with some risk avoidance strategy (explicit or implicit). It is hardly surprising that various standards are different, even quite different. More,rather than fewer, concepts are available to those who develop structural standards. In the absence of a binding philosophy of structural behaviour, there will continue to be divergence along the way to improved standards. It must be appreciated that all current standards “work”. Any of the current naval and commercial ship design approaches can be used to produce structural designs that function with adequate reliability over a 20+ yearlife expectancy, unless subjected to poor maintenance, human operational error, or deliberate damage. Changes to standards are, therefore, resisted by all those who have invested time and effort in them as developers and users. The rationale for change must be presented well, and its benefits have to outweigh its costs.Experienced designers recognize that structural behaviour can be very complex. Despite this, it is necessary to use simple, practical approaches in design standards, to avoid adding to the problem through overly-complex rules that are difficult to apply and more so to check and audit. Stress is the primary load-effect that standards focus on, partly because it is so readily calculated. The main concerns are material yielding, buckling and fatigue. All of these are local behaviours, and all are used as surrogates for actual structural failure. A structure is a system, comprised of elements, which in turn are built from materials.As an example, yielding can be considered. Yielding is a material level‘failure’, very common, usually very localized, and usually producing noobservable effect. It can be quite irrelevant. The important issue is the behaviour and failure of the structural system, even at the level of the structural components. Ship structures are especially redundant structures, quite unlike most civil structures and buildings. Ship structures are exposed to some of the harshest loading regimes, yet are usually capable of tolerating extensive material and component failure, prior to actual structural collapse.An essential deficiency of all traditional structural standards has been the failure to consider the structural redundancy (path to failure) and identify weaknesses in the system. Areas of weakness are normally defined as those parts that will first yield or fail.However, far more important is the ability of the structure to withstand these and subsequent local/material failures and redistribute the load. The real weaknesses are a lack of secondary load paths. It is often assumed, wrongly, that initial strength is a valid indicator for ultimate strength, and far simpler to assess. There is a need to focus on ways of creating robust structures, much as we use subdivision to create adequate damage stability. As another example, consider frames under lateral loads. When designed properly, frames can exhibit not only sufficient initial strength, but substantial reserve strength, due to the secondary load path created by axial stresses in the plate and frame. In effect, it is possible to create a ductile structure (analogous to a ductile material). If we instead use current designstandards that emphasize elastic section modulus, we risk creating a‘brittle’ structure, even w hen built from ductile materials.In the case of fatigue and buckling, it is again necessary to stand back from consideration of the initial effects, and examine whether there is sufficient reserve (secondary load paths). When there is no such reserve, there is the structural equivalent of a subdivision plan that cannot tolerate even one compartment flooding.The above discussion talks only about structural response, and indicated some gaps. Similar gaps exist in our knowledge of loads. The complexity of ship structures, the complexity of the loads that arise in a marine environment, and the dominating influence of human factors in any risk assessment for vessels, all present daunting challenges.The project team’s approach to this project, described in the following sections, has intended to provide part of the basis for future design standard development.1.1集装箱运输的变化当商业已成为并将继续更加国际化，远洋集装箱运输已成为成倍增长的将任何种类的货物从一个港口移到另一个港口的手段。

船舶与海洋工程论文中英文资料外文翻译文献

中英文资料外文翻译文献A Simple Prediction Formula of Roll Damping of Conventional Cargo Ships on the Basis of lkeda's Method and Its LimitationSince the roll damping of ships has significant effects of viscosity, it is difficult to calculate it theoretically. Therefore, experimental results or some prediction methods are used to get the roll damping in design stage of ships. Among some prediction methods, Ikeda’s one is widely used in many ship motion computer programs. Using the method, the roll damping of various ship hulls with various bilge keels can be calculated to investigate its characteristics. To calculate the roil damping of each ship, detailed data of the ship are needed to input. Therefore, a simpler prediction method is expected in primary design stage. Such a simple method must be useful to validate the results obtained by a computer code to predict it on the basis of Ikeda，s method, too. On the basis of the predicted roll damping by Ikeda’s method for various ships, a very simple prediction formula of the roll damping of ships is deduced in the present paper. Ship hull forms are systematically changed by changing length, beam, draft, mid-ship sectional coefficient and prismatic coefficient. It is found, however, that this simple formula can not be used for ships that have high position of the center of gravity. A modified method to improve accuracy for such ships is proposed.Key words: Roll damping, simple prediction formula, wave component, eddy component, bilge keel component.IntroductionIn 1970s, strip methods for predicting ship motions in 5-degree of freedoms in waves have been established. The methods are based on potential flow theories (Ursell-Tasai method, source distribution method and so on), and can predict pitch, heave, sway and yaw motions of ships in waves in fairly good accuracy. In roll motion, however, the strip methods do not work well because of significant viscous effects on the roll damping. Therefore, some empirical formulas or experimental dataare used to predict the roll damping in the strip methods.To improve the prediction of roll motions by these strip methods, one of the authors carried out a research project to develop a roll damping prediction method which has the same concept and the same order of accuracy as the strip methods which are based on hydrodynamic forces acting on strips. The review of the prediction method was made by Himeno [5] and Ikeda [6，7] with the computer program.The prediction method, which is now called Ikeda’s method, divides the roll damping into the frictional (BF), the wave (Bw)，the eddy (Be) and the bilge keel (Bbk) components at zero forward speed, and at forward speed, the lift (Bi) is added. Increases of wave and friction components due to advance speed are also corrected on the basis of experimental results. Then the roll damping coefficient B44 (= roll damping moment (kgfm)/roll angular velocity (rad/sec)) can be expressed as follows: B44 B bk (1)At zero forward speed, each component except the friction and lift components are predicted for each cross section with unit length and the predicted values are summed up along the ship length. The friction component is predicted by Kato’s formula for a three-dimensional ship shape. Modification functions for predicting the forward speed effects on the roll damping components are developed for the friction, wave and eddy components. The computer program of the method was published, and the method has been widely used.For these 30 years, the original Ikeda’s method developed for conven tional cargo ships has been improved to apply many kinds of ships, for examples, more slender and round ships, fishing boats, barges, ships with skegs and so on. The original method is also widely used. However, sometimes, different conclusions of roll mot ions were derived even though the same Ikeda’s method was used in the calculations. Then, to check the accuracy of the computer programs of the same Ikeda’s method, a more simple prediction method with the almost same accuracy as the Ikeda’s original one h as been expected to be developed. It is said that in design stages of ships, Ikeda’s method is too complicated to use. To meet these needs, a simple roll damping prediction method was deduced by using regression analysis [8].Previous Prediction FormulaThe simple prediction formula proposed in previous paper can not be used for modem ships that have high position of center of gravity or long natural roll period such as large passenger ships with relatively flat hull shape. In order to investigate its limitation, the authors compared the result of this prediction method with original Ikeda’s one while out of its calculating limitation. Fig. 1 shows the result of the comparison with their method of roll damping. The upper one is on the condition that the center of gravity is low and the lower one on the condition that the center of gravity is high.From this figure, the roll damping estimated by this prediction formula is in good agreement with the roll damping calculated by the Ikeda’s method for low positi on of center of gravity, but the error margin grows for the high position of center of gravity. The results suggest that the previous prediction formula is necessary to be revised. Methodical Series ShipsModified prediction formula will be developed on the basis of the predicted results by Ikeda’s method using the methodical series ships. This series ships are constructed based on the Taylor Standard Series and its hull shapes are methodically changed by changing length, beam, draft, midship sectional coefficient and longitudinal prismatic coefficient. The geometries of the series ships are given by the following equations. Proposal of New Prediction Method of Roll DampingIn this chapter, the characteristics of each component of the roll damping, the frictional, the wave, the eddy and the bilge keel components at zero advanced speed, are discussed, and a simple prediction formula of each component is developed.As well known, the wave component of the roll damping for a two-dimensional cross section can be calculated by potential flow theories in fairly good accuracy. In Ikeda's method, the wave damping of a strip section is not calculated and the calculated values by any potential flow theories are used as the wave damping.reason why viscous effects are significant in only roll damping can be explained as follows. Fig. 4 shows the wave component of the roll damping for 2-D sections calculated by a potential flow theory.ConclusionsA simple prediction method of the roll damping of ships is developed on the basis of the Ikeda’s original prediction method which was developed in the same concept as a strip method for calculating ship motions in waves. Using the data of a ship, B/d, Cb，Cm, OG/d, G)，bBK/B, Ibk/Lpp，(pa, the roll damping of a ship can be approx imately predicted. Moreover, the limit of application of Ikeda’s prediction method to modern ships that have buttock flow stern is demonstrated by the model experiment. The computer program of the method can be downloaded from the Home Page of Ikeda’s Labo (AcknowledgmentsThis work was supported by the Grant-in Aid for Scientific Research of the Japan Society for Promotion of Science (No. 18360415).The authors wish to express sincere appreciation to Prof. N. Umeda of Osaka University for valuable suggestions to this study.References五、Y. Ikeda, Y. Himeno, N. Tanaka, On roll damping force of shipEffects of friction of hull and normal force of bilge keels, Journal of the Kansai Society of Naval Architects 161 (1976) 41-49. (in Japanese)六、Y. Ikeda, K. Komatsu, Y. Himeno, N. Tanaka, On roll damping force of ship~Effects of hull surface pressure created by bilge keels, Journal of the Kansai Society of Naval Architects 165 (1977) 31-40. (in Japanese)七、Y. Ikeda, Y. Himeno, N. Tanaka, On eddy making component of roll damping force on naked hull, Journal of the Society of Naval Architects 142 (1977) 59-69. (in Japanese)八、Y. Ikeda, Y. Himeno, N. Tanaka, Components of roll damping of ship at forward speed, Journal of the Society of Naval Architects 143 (1978) 121-133. (in Japanese) 九、Y. Himeno, Prediction of Ship Roll Damping一State of the Art, Report of Department of Naval Architecture & Marine Engineering, University of Michigan, No.239, 1981.十、Y. Ikeda, Prediction Method of Roll Damping, Report of Department of Naval Architecture, University of Osaka Prefecture, 1982.十一、Y. Ikeda, Roll damping, in: Proceedings of 1stSymposium of Marine Dynamics Research Group, Japan, 1984, pp. 241-250. (in Japanese)十二、Y. Kawahara, Characteristics of roll damping of various ship types and as imple prediction formula of roll damping on the basis of Ikeda’s method, in: Proceedings of the 4th Asia-Pacific Workshop on Marine Hydrodymics, Taipei, China, 2008，pp. 79-86.十三、Y. Ikeda, T. Fujiwara, Y. Himeno, N. Tanaka, Velocity field around ship hull in roll motion, Journal of the Kansai Society of Naval Architects 171 (1978) 33-45. (in Japanese)十四、N. Tanaka, Y. Himeno, Y. Ikeda, K. Isomura,Experimental study on bilge keel effect for shallow draftship, Journal of the Kansai Society of Naval Architects 180 (1981) 69-75. (in Japanese)常规货船的横摇阻尼在池田方法基础上的一个简单预测方法及其局限性摘要：由于船的横摇阻尼对其粘度有显着的影响，所以很难在理论上计算。

船舶专业外文文献之欧阳物创编

Spatial scheduling forlargeassemblyblocks inshipbuildingAbstract: This paper addresses the spatial scheduling problem (SPP) for large assembly blocks, which arises in a shipyard assembly shop. The spatial scheduling problem is to schedule a set of jobs, of which each requires its physical space in a restricted space. This problem is complicated because both the scheduling of assemblies with different due dates and earliest starting times and the spatial allocation of blocks with different sizes and loads must be considered simultaneously. Thisproblem under consideration aims to the minimization of both the makespan and the load balance and includes variousreal-world constraints, which includes the possible directional rotation of blocks, the existence of symmetric blocks, and the assignment of some blocks to designated workplaces or work teams. The problem is formulated as a mixed integer programming (MIP) model and solved by a commercially available solver. A two-stage heuristic algorithm has been developed to use dispatching priority rules and a diagonal fill space allocation method, which is a modification ofbottom-left-fill space allocation method. The comparison and computational results shows the proposed MIP model accommodates various constraints and the proposed heuristic algorithm solves the spatial scheduling problems effectively and efficiently.Keywords:Large assembly block; Spatial scheduling; Load balancing; Makespan; Shipbuilding1. IntroductionShipbuilding is a complex production process characterized by heavy and large parts, various equipment, skilled professionals, prolonged lead time, and heterogeneous resource requirements. The shipbuilding process is divided into subprocesses in the shipyard, including ship design, cutting and bending operations, block assembly, outfitting, painting,pre-erection and erection. The assembly blocks are called the minor assembly block, the sub assembly block, and the large assembly block according to their size and progresses in the course of assembly processes. This paper focuses on the spatial scheduling problem of large assembly blocks in assembly shops. Fig. 1 shows a snapshot of large assembly blocks in a shipyard assembly shop.Recently, the researchers and practitioners at academia and shipbuilding industries recently got together at “Smart Production Technology Forum in Shipbuilding and Ocean Plant Industries” to recognize that there are various spatial scheduling problems in every aspect of shipbuilding due to the limited space, facilities, equipment, labor and time. The SPPs occur in various working areas such as cutting and blast shops, assembly shops, outfitting shops, pre-erection yard, and dry docks. The SPP at different areas has different requirements and constraints to characterize the unique SPPs. In addition, the depletion of energy resources on land put more emphasis on the ocean development. The shipbuilding industries face the transition of focus from the traditionalshipbuilding to ocean plant manufacturing. Therefore, the diversity of assembly blocks, materials, facilities and operations in ship yards increases rapidly.There are some solution providers such as Sie mens™ and Dassult Systems™ to provide integrated software including product life management, enterprise resource planning system, simulation and etc. They indicated the needs of efficient algorithms to solve medium- to large-sized SPP problems in 20 min, so that the shop can quickly re-optimize the production plan upon the frequent and unexpected changes in shop floors with the ongoing operations on exiting blocks intact.There are many different applications which require efficient scheduling algorithms with various constraints and characteristics (Kim and Moon, 2003, Kim et al., 2013, Nguyen and Yun, 2014 and Yan et al., 2014). However, the spatial scheduling problem which considers spatial layout and dynamic job scheduling has not been studied extensively. Until now, spatial scheduling has to be carried out by human schedulers only with their experiences and historical data. Even when human experts have much experience in spatial scheduling, it takes a long time and intensive effort to producea satisfactory schedule, due to the complexity of considering blocks’ geometric shapes, loads, required facilities, etc. In practice, spatial scheduling for more than a six-month period is beyond the human schedulers’ capacity. Moreover, the space in the working areas tends to be the most critical resource in shipbuilding. Therefore, the effective management of spatial resources through automation of the spatial scheduling process is a critical issue in the improvement of productivity in shipbuilding plants.A shipyard assembly shop is consisted of pinned workplaces, equipment, and overhang cranes. Due to the heavy weight of large assembly block, overhang cranes are used to access any areas over other objects without any hindrance in the assembly shop. The height of cranes can limit the height of blocks that can be assembled in the shop. The shop can be considered as a two-dimensional space. The blocks are placed on precisely pinned workplaces.Once the block is allocated to a certain area in a workplace, it is desirable not to move the block again to different locations due to the size and weight of the large assembly blocks. Therefore, it is important to allocate the workspace to each block carefully, so that the workspace in an assembly shop canbe utilized in a most efficient way. In addition, since each block has its due date which is pre-determined at the stage of ship design, the tardiness of a block assembly can lead to severe delay in the following operations. Therefore, in the spatial scheduling problem for large assembly blocks, the scheduling of assembly processes for blocks and the allocation of blocks to specific locations in workplaces must be considered at the same time. As the terminology suggests, spatial scheduling pursues the optimal spatial layout and the dynamic schedule which can also satisfy traditional scheduling constraints simultaneously. In addition, there are many constraints or requirements which are serious concerns on shop floors and these complicate the SPP. The constraints or requirements this study considered are explained here: (1) Blocks can be put in either directions, horizontal or vertical. (2) Since the ship is symmetric around the centerline, there exist symmetric blocks. These symmetric blocks are required to be put next to each other on the same workplace. (3) Some blocks are required to be put on a certain special area of the workplace, because the work teams on that area has special equipment or skills to achieve a certain level of quality or complete the necessary tasks. (4) Frequently, the production plan may not beimplemented as planned, so that frequent modifications in production plans are required to cope with the changes in the shop. At these modifications, it is required to produce a new modified production plan which does not remove or move the pre-existing blocks in the workplace to complete the ongoing operations. (5) If possible at any time, the load balancing over the work teams, i.e., workplaces are desirable in order to keep all task assignments to work teams fair and uniform.Lee, Lee, and Choi (1996) studied a spatial scheduling that considers not only traditional scheduling constraints like resource capacity and due dates, but also dynamic spatial layout of the objects. They used two-dimensional arrangement algorithm developed by Lozano-Perez (1983) to determine the spatial layout of blocks in shipbuilding. Koh, Park, Choi, and Joo (1999) developed a block assembly scheduling system for a shipbuilding company. They proposed a two-phase approach that includes a scheduling phase and a spatial layout phase. Koh, Eom, and Jang (2008) extended their precious works (Koh et al., 1999) by proposing the largest contact area policy to select a better allocation of blocks. Cho, Chung, Park, Park, and Kim (2001) proposed a spatial scheduling system for block painting process in shipbuilding, including blockscheduling, four arrangement algorithms and block assignment algorithm. Park et al. (2002) extended Cho et al. (2001) utilizing strategy simulation in two consecutive operations of blasting and painting. Shin, Kwon, and Ryu (2008) proposed a bottom-left-fill heuristic method for spatial planning of block assemblies and suggested a placement algorithm for blocks by differential evolution arrangement algorithm. Liu, Chua, and Wee (2011) proposed a simulation model which enabled multiple priority rules to be compared. Zheng, Jiang, and Chen (2012) proposed a mathematical programming model for spatial scheduling and used several heuristic spatial scheduling strategies (grid searching and genetic algorithm). Zhang and Chen (2012) proposed another mathematical programming model and proposed the agglomeration algorithm.This study presents a novel mixed integer programming (MIP) formulation to consider block rotations, symmetrical blocks, pre-existing blocks, load balancing and allocation of certain blocks to pre-determined workspace. The proposed MIP models were implemented by commercially available software, LINGO® and problems of various sizes are tested. The computational results show that the MIP model is extremelydifficult to solve as the size of problems grows. To efficiently solve the problem, a two-stage heuristic algorithm has been proposed.Section 2 describes spatial scheduling problems and assumptions which are used in this study. Section 3 presents a mixed integer programming formulation. In Section 4, atwo-stage heuristic algorithm has been proposed, including block dispatching priority rules and a diagonal fill space allocation heuristic method, which is modified from the bottom-left-fill space allocation method. Computational results are provided in Section 5. The conclusions are given in Section 6.2. Problem descriptionsThe ship design decides how to divide the ship into many smaller pieces. The metal sheets are cut, blast, bend and weld to build small blocks. These small blocks are assembled to bigger assembly blocks. During this shipbuilding process, all blocks have their earliest starting times which are determined from the previous operational step and due dates which are required by the next operational step. At each step, the blocks have their own shapes of various sizes and handling requirements. During the assembly, no block can overlapphysically with others or overhang the boundary of workplace. The spatial scheduling problem can be defined as a problem to determine the optimal schedule of a given set of blocks and the layout of workplaces by designating the blocks’ workplace simultaneously. As the term implies, spatial scheduling pursues the optimal dynamic spatial layout schedule which can also satisfy traditional scheduling constraints. Dynamic spatial layout schedule can be including the spatial allocation issue, temporal allocation issue and resource allocation issue.An example of spatial scheduling is given in Fig. 2. There are 4 blocks to be allocated and scheduled in a rectangular workplace. Each block is shaded in different patterns. Fig. 2 shows the 6-day spatial schedule of four large blocks on a given workplace. Blocks 1 and 2 are pre-existed or allocated at day 1. The earliest starting times of blocks 3 and 4 are days 2 and 4, respectively. The processing times of blocks 1, 2 and 3 are 4, 2 and 4 days, respectively.The spatial schedule must satisfy the time and space constraints at the same time. There are many objectives in spatial scheduling, including the minimization of makespan, the minimum tardiness, the maximum utilization of spatial andnon-spatial resources and etc. The objective in this study is to minimize the makespan and balance the workload over the workspaces.There are many constraints for spatial scheduling problems in shipbuilding, depending on the types of ships built, the operational strategies of the shop, organizational restrictions and etc. Some basic constraints are given as follows; (1) all blocks must be allocated on given workplaces for assembly processes and must not overstep the boundary of the workplace; (2) any block cannot overlap with other blocks; (3) all blocks have their own earliest starting time and due dates;(4) symmetrical blocks needs to be placed side-by-side in the same workspace. Fig. 3 shows how symmetrical blocks need to be assigned; (5) some blocks need to be placed in the designated workspace; (6) there can be existing blocks before the planning horizon; (7) workloads for workplaces needs to be balanced as much as possible.In addition to the constraints described above, the following assumptions are made.(1)The shape of blocks and workplaces is rectangular.(2)Once a block is placed in a workplace, it cannot be movedor removed from its location until the process is completed.(3)Blocks can be rotated at angles of 0° and 90° (see Fig. 4).(4)The symmetric blocks have the same sizes, are rotated at the same angle and should be placed side-by-side on the same workplace.(5)The non-spatial resources (such as personnel or equipment) are adequate.3. A mixed integer programming modelA MIP model is formulated and given in this section. The objective function is to minimize makespan and the sum of deviation from average workload per workplace, considering the block rotation, the symmetrical blocks, pre-existing blocks, load balancing and the allocation of certain blocks topre-determined workspace.A workspace with the length LENW and the width WIDW is considered two-dimensional rectangular space. Since the rectangular shapes for the blocks have been assumed, a block can be placed on workspace by determining (x, y) coordinates, where 0 ⩽ x ⩽ LENW and 0 ⩽ y ⩽ WIDW. Hence, the dynamic layout of blocks on workplaces is similar to two-dimensional bin packing problem. In addition to the block allocation, theoptimal schedule needs to be considered at the same time in spatial scheduling problems. Z axis is introduced to describe the time dimension. Then, spatial scheduling problem becomes a three-dimensional bin packing problem with various objectives and constraints.The decision variables of spatial scheduling problem are (x, y, z) coordinates of all blocks within a three-dimensional space whose sizes are LENW, WIDW and T in x, y and z axes, where T represents the planning horizon. This space is illustrated in Fig. 5.In Fig. 6, the spatial scheduling of two blocks into a workplace is illustrated as an example. The parameters p1 and p2 indicate the processing times for Blocks 1 and 2, respectively. As shown in z axis, Block 2 is scheduled after Block 1 is completed.4. A two-stage heuristic algorithmThe computational experiments for the MIP model in Section 3 have been conducted using a commercially available solver, LINGO®. Obtaining global optimum solutions is very time consuming, considering the number of variables and constraints. A ship is consisted of more than 8 hundred large blocks and the size of problem using MIP model is beyondtoday’s computational ability. A two-stage heuristic algorithm has been proposed using the dispatching priority rules and a diagonal fill method.4.1. Stage 1: Load balancing and sequencingPast research on spatial scheduling problems considers various priority rules. Lee et al. (1996) used a priority rule for the minimum slack time of blocks. Cho et al. (2001) and Park et al. (2002) used the earliest due date. Shin et al. (2008) considered three dispatching priority rules for start date, finish date and geometric characteristics (length, breadth, and area) of blocks. Liu and Teng (1999) compared 9 different dispatching priority rules including first-come first-serve, shortest processing time, least slack, earliest due date, critical ratio, most waiting time multiplied by tonnage, minimal area residue, and random job selection. Zheng et al. (2012) used a dispatching rule of longest processing time and earliest start time.Two priority rules are used in this study to divide all blocks into groups for load balancing and to sequence them considering the due date and earliest starting time. Two priority rules are streamlined to load-balance and sequence the blocks into an algorithm which is illustrated in Fig. 7. The first step of the algorithm in this stage is to group the blocks based on theurgency priority. The urgency priority is calculated by subtracting the earliest starting time and the processing time from the due date for each block. The smaller the urgency priority, the more urgent the block needs to bed scheduled. Then all blocks are grouped into an appropriate number of groups for a reasonable number of levels in urgency priorities. Let g be this discretionary number of groups. There are g groups of blocks based on the urgency of blocks. The number of blocks in each group does not need to be identical.Blocks in each group are re-ordered grouped into as many subgroups as workplaces, considering the workload of blocks such as the weight or welding length. The blocks in each subgroup have the similar urgency and workloads. Then, these blocks in each subgroup are ordered in an ascending order of the earliest starting time. This ordering will be used to block allocations in sequence. The subgroup corresponds to the workplace.If block i must be processed at workplace w and is currently allocated to other workplace or subgroup than w, block i is swapped with a block at the same position of block i in an ascending order of the earliest starting time at its workplace (or subgroup). Since the symmetric blocks must be located ona same workplace, a similar swapping method can be used. One of symmetric blocks which are allocated into different workplace (or subgroups) needs to be selected first. In this study, we selected one of symmetric blocks whichever has shown up earlier in an ascending order of the earliest starting time at their corresponding workplace (or subgroup). Then, the selected block is swapped with a block at the same position of symmetric blocks in an ascending order of the earliest starting time at its workplace (or subgroups).4.2. Stage 2: Spatial allocationOnce the blocks in a workplace (or subgroup) are sequentially ordered in different urgency priority groups, each block can be assigned to workplaces one by one, and allocated to a specific location on a workplace. There has been previous research on heuristic placement methods. The bottom-left (BL) placement method was proposed by Baker, Coffman, and Rivest (1980) and places rectangles sequentially in a bottom-left most position. Jakobs (1996) used a bottom-left method that is combined with a hybrid genetic algorithm (see Fig. 8). Liu and Teng (1999) developed an extended bottom-left heuristic which gives priority to downward movement, where the rectangles is only slide leftwards if no downward movement ispossible. Chazele (1983) proposed the bottom-left-fill (BLF) method, which searches for lowest bottom-left point, holes at the lowest bottom-left point and then place the rectangle sequentially in that bottom-left position. If the rectangle is not overlapped, the rectangle is placed and the point list is updated to indicate new placement positions. If the rectangle is overlapped, the next point in the point list is selected until the rectangle can be placed without any overlap. Hopper and Turton (2000) made a comparison between the BL and BLF methods. They concluded that the BLF method algorithm achieves better assignment patterns than the BL method for Hopper’s example problems.Spatial allocation in shipbuilding is different fromtwo-dimensional packing problem. Blocks have irregular polygonal shapes in the spatial allocation and blocks continuously appear and disappear since they have their processing times. This frequent placement and removal of blocks makes BLF method less effective in spatial allocation of large assembly block.In order to solve these drawbacks, we have modified the BLF method appropriate to spatial scheduling for large assembly blocks. In a workplace, since the blocks are placed andremoved continuously, it is more efficient to consider both the bottom-left and top-right points of placed blocks instead of bottom-left points only. We denote it as diagonal fill placement (see Fig. 9). Since the number of potential placement considerations increases, it takes a bit more time to implement diagonal fill but the computational results shows that it is negligible.The diagonal fill method shows better performances than the BLF method in spatial scheduling problems. When the BLF method is used in spatial allocation, the algorithm makes the allocation of some blocks delayed until the interference bypre-positioned blocks are removed. It generates a less effective and less efficient spatial schedule. The proposed diagonal fill placement method resolve this delays better by allocating the blocks as soon as possible in a greedy way, as shown in Fig. 10. The potential drawbacks from the greedy approaches is resolved by another placement strategy to minimize the possible dead spaces, which will be explained in the following paragraphs.The BLF method only focused on two-dimensional bin packing. Frequent removal and placement of blocks in a workspace may lead to accumulation of dead spaces, which are small andunusable spaces among blocks. A minimal possible-dead space strategy has been used along with the BLF method. Possible-dead spaces are being generated over the spatial scheduling and they have less chance to be allocated for future blocks. The minimal possible-dead space strategy minimizes the potential dead space after allocating the following blocks (Chung, 2001 and Koh et al., 2008) by considering the 0° and 90° rotation of the block and allocating the following block for minimal possible-dead space. Fig. 11 shows an example of three possible-dead space calculations using the neighbor block search method. When a new scheduling block is considered to be allocated, the rectangular boundary of neighboring blocks and the scheduling blocks is searched. This boundary can be calculated by obtaining the smallest and the largest x and y coordinates of neighboring blocks and the scheduling blocks. Through this procedure, the possible-dead space can be calculated as shown in Fig. 11. Considering the rotation of the scheduling blocks and the placement consideration points from the diagonal fill placement methods, the scheduling blocks will be finally allocated.In this two-stage algorithm, blocks tend to be placed adjacent to one of the alternative edges and their assignments are donepreferentially to minimize fractured spaces.5. Computational resultsTo demonstrate the effectiveness and efficiency of the proposed MIP formulation and heuristic algorithm, the actual data about 800+ large assembly blocks from one of major shipbuilding companies has been obtained and used. All test problems are generated from this real-world data.All computational experiments have been carried out on a personal computer with a Intel® Core™ i3-2100 CPU @3.10 GHz with 2 GB RAM. The MIP model in Section 3 has been programmed and solved using LINGO® version 10.0, a commercially available software which can solve linear and nonlinear models. The proposed two-stage heuristic algorithm has been programmed in JAVA programming language. Because our computational efforts to obtain the optimal solutions for even small problems are more than significant, the complexity of SPP can be recognized as one of most difficult and time consuming problems.Depending on the scaling factor α in objective function of the proposed MIP formulation, the performance of the MIP model varies significantly. Setting α less than 0.01 makes the loadbalancing capability to be ignored from the optimal solution in the MIP model. For computational experiments in this study, the results with the scaling factor set to 0.01 is shown and discussed. The value needs to be fine-turned to obtain the desirable outcomes.Table 1 shows a comparison of computational results and performance between the MIP models and two-stage heuristic algorithm. As shown in Table 1, the proposed two-stage heuristic algorithm finds the near-optimal solutions for medium and large problems very quickly while the optimal MIP models was not able to solve the problems of medium or large sizes due to the memory shortage on computers. It is observed that the computational times for the MIP problems are rapidly growing as the problem sizes increases. The test problems in Table 1 have 2 workplaces.Table 1.Computational results and performance between the MIP models and two-stage heuristic algorithm.The MIP model Two-stage heuristic algorithmNumber of blocksOptimal solution Time (s) Best known solution Time (s)10 12.360 1014.000 12.360 0.026The MIP model Two-stage heuristic algorithmNumber of blocksOptimal solution Time (s) Best known solution Time (s)20 22.380a 38250.000 21.380 0.07830 98.344a 38255.000 30.740 0.21850 ––53.760 0.719100 ––133.780 2.948200 ––328.860 12.523300 ––416.060 40.154400 ––532.360 73.214Best feasible solution after 10 h in Global Solver of LINGO®. Full-size tableTable optionsView in workspaceDownload as CSVThe optimal solutions for test problems with more than 50 blocks in Table 1 have been not obtained even after 24 h. The best known feasible solutions after 10 h for the test problems with 20 blocks and 30 blocks are reported in Table 1. It is observed that the LINGO® does not solve the nonlinear constraints very well as shown in Table 1. For very small problem with 10 blocks, the LINGO® was able to achieve the optimal solutions. For slightly bigger problems, the LINGO®took significantly more time to find feasible solutions. From this observation, the approaches to obtain the lower bound through the relaxation method and upper bounds are significant required in future research.In contrary, the proposed two-stage heuristic algorithm was able to find the good solutions very quickly. For the smallest test problem with 10 blocks, it was able to find the optimal solution as well. The computational times are 1014 and 0.026 s, respectively, for the MIP approach and the proposed algorithm. Interestingly, the proposed heuristic algorithm found significantly better solutions in only 0.078 and 0.218 s, respectively, for the test problems with 20 and 30 blocks. For these two problems, the LINGO® generates the worse solutions than the heuristics after 10 h of computational times. The symbol ‘–’ in Table 1 indicates that the Global Solver of LINGO® did not find the feasible solutions.Another observation on the two-stage heuristic algorithms is the robust computational times. The computation times does not change much as the problem sizes increase. It is because the simple priority rules are used without considering many combinatorial configurations.Fig. 12 shows partial solutions of test problems with 20 and 30blocks on 2 workplaces. The purpose of Fig. 12 is to show the progress of production planning generated by the two-stage heuristic algorithm. Two workplaces are in different sizes of (40, 30) and (35, 40), respectively.6. ConclusionsAs global warming is expected to open a new way to transport among continent through North Pole Sea and to expedite the oceans more aggressively, the needs for more ships and ocean plants are forthcoming. The shipbuilding industries currently face increased diversity of assembly blocks in limited production shipyard. Spatial scheduling for large assembly blocks holds the key role in successful operations of the shipbuilding companies.The task of spatial scheduling takes place at almost every stage of shipbuilding processes and the large assembly shop is one of the mos t congested operational areas in today’s shipbuilding. It is also known that the spatial scheduling problem has been the major source of the bottleneck. The practitioners in shipbuilding industries requires their production planning system to optimize the spatial scheduling and to respond quickly to the changes on the shop floor byre-optimizing the production plan in 20-min time frame. Most。

船舶翻译资料

溶融亜铅メッキ寸法すんぽうプロックステージ配付はいふ別途べっとマンホールmannho-ru タンク溶接ようせつアーク溶接アセチレン溶接鉄パイプを電気溶接するビードスラグスラグ・ウールスパッタエッジボルトボルト面取りめんとり載るのるブラストグレーシルバー・グレーパール・グレーナット手配てはいメーカーパッキン仮纏めるまとめる荷札にふだ取り付けとりつけ重ねかさね重ねてかさねて納期のうきショット錆さび仕上げしあげ客先きゃくさき入念にゅうねんグラインダ一grinder歪みひずみ除くのぞく鋼はがね鋼リムドこう鋼ベイナイトこうジンク開先かいさき矢や矢くさび打ち込みうちこみロッキング返りかえりライナ一発条ばね座金ざがね据付すえつけジンクリッチぺイン卜hatchグリースカバースチフナースケッチハッチポンチ外れるはずれるコーミング倣うならうコストダウン生じるしょうじる都度つどcoaminghandrailstanchions手摺柱deckcamber継手つぎてニップルstiffenerステンレスsheergalvanizedzinc rich paintantennaアイプレート紐ひもパレットグラインダーシールフランジ振り分けるふりわけるホワイトインターナショナル水張りみずばり張りはり張りガラスばりレーダーマストarrangement納入のうにゅう組み立てくみたて床板ゆかいた根太ねだライトグレー枠フライホイールスティフナーブラケットプライマ一パーカーライジングポリエチレンライニング目盛めもりペイントガルバナイトアルミハンガーHOT DIPPED GALVANIZED 熔化的镀锌层,在某种金属外面涂上镀锌的时候所用尺寸，尺码，长短，大小组件stage ,舞台，讲坛阶段，程度（段階）分发manhole,进人孔,工作口,升降口.下水道口tank,坦克〔容器〕罐，筒，大桶，槽，箱。

介绍船体英语作文带翻译

介绍船体英语作文带翻译Introduction to Ship's Hull。

The hull of a ship is the main body of the vessel,which provides buoyancy and stability in the water. It is typically made of steel, aluminum, or composite materials, and is designed to withstand the forces of the sea and the weight of the cargo or passengers it carries. The hull is divided into several compartments, each with its ownpurpose and function, such as the bow, stern, and midship sections.船体是船舶的主体，它在水中提供浮力和稳定性。

通常由钢铁、铝或复合材料制成，设计用于承受海洋的力量和承载货物或乘客的重量。

船体分为几个舱室，每个舱室都有自己的目的和功能，如船头、船尾和船中部等。

The bow, or front of the ship, is designed to cut through the water and reduce resistance, allowing the shipto move forward efficiently. The stern, or rear of the ship,is responsible for steering and propulsion, and is often equipped with a rudder and propeller to control the direction and speed of the vessel. The midship section is the central part of the hull, where the main cargo holds and passenger accommodations are located.船头，或船的前部，设计用于切割水流，减少阻力，使船只能够高效前进。

船舶专业外文文献之欧阳地创编

Spatial scheduling for largeassemblyblocks inshipbuildingAbstract: This paper addresses the spatial scheduling problem (SPP) for large assembly blocks, which arises in a shipyard assembly shop. The spatial scheduling problem is to schedule a set of jobs, of which each requires its physical space in a restricted space. This problem is complicated because both the scheduling of assemblies with different due dates and earliest starting times and the spatial allocation of blocks with different sizes and loads must be considered simultaneously. This problem under consideration aims to the minimization of both the makespan and the load balance and includes various real-world constraints, which includes thepossible directional rotation of blocks, the existence of symmetric blocks, and the assignment of some blocks to designated workplaces or work teams. The problem is formulated as a mixed integer programming (MIP) model and solved by a commercially available solver. A two-stage heuristic algorithm has been developed to use dispatching priority rules and a diagonal fill space allocation method, which is a modification of bottom-left-fill space allocation method. The comparison and computational results shows the proposed MIP model accommodates various constraints and the proposed heuristic algorithm solves the spatial scheduling problems effectively and efficiently.Keywords:Large assembly block; Spatial scheduling; Load balancing; Makespan; Shipbuilding1. IntroductionShipbuilding is a complex production process characterized by heavy and large parts, various equipment, skilled professionals, prolonged lead time, and heterogeneous resource requirements. Theshipbuilding process is divided into sub processes in the shipyard, including ship design, cutting and bending operations, block assembly, outfitting, painting, pre-erection and erection. The assembly blocks are called the minor assembly block, the sub assembly block, and the large assembly block according to their size and progresses in the course of assembly processes. This paper focuses on the spatial scheduling problem of large assembly blocks in assembly shops. Fig. 1 shows a snapshot of large assembly blocks in a shipyard assembly shop. Recently, the researchers and practitioners at academia and shipbuilding industries recently got together at “Smart Production Technology Forum in Shipbuilding and Ocean Plant Industries” to recognize that there are various spatial scheduling problems in every aspect of shipbuilding due to the limited space, facilities, equipment, labor and time. The SPPs occur in various working areas such as cutting and blast shops, assembly shops, outfitting shops, pre-erection yard, and dry docks. The SPP atdifferent areas has different requirements and constraints to characterize the unique SPPs. In addition, the depletion of energy resources on land put more emphasis on the ocean development. The shipbuilding industries face the transition of focus from the traditional shipbuilding to ocean plant manufacturing. Therefore, the diversity of assembly blocks, materials, facilities and operations in ship yards increases rapidly.There are some solution providers such as Siemens™and Dassult Systems™to provide integrated software including product life management, enterprise resource planning system, simulation and etc. They indicated the needs of efficient algorithms to solve medium- to large-sized SPP problems in 20 min, so that the shop can quickly re-optimize the production plan upon the frequent and unexpected changes in shop floors with the ongoing operations on exiting blocks intact.There are many different applications which require efficient scheduling algorithms with variousconstraints and characteristics (Kim and Moon, 2003, Kim et al., 2013, Nguyen and Yun, 2014 and Yan et al., 2014). However, the spatial scheduling problem which considers spatial layout and dynamic job scheduling has not been studied extensively. Until now, spatial scheduling has to be carried out by human schedulers only with their experiences and historical data. Even when human experts have much experience in spatial scheduling, it takes a long time and intensive effort to produce a satisfactory schedule, due to the complexity of considering blocks’ geometric shapes, loads, required facilities, etc. In practice, spatial scheduling for more than a six-month period is beyond the human schedulers’ capacity. Moreover, the space in the working areas tends to be the most critical resource in shipbuilding. Therefore, the effective management of spatial resources through automation of the spatial scheduling process is a critical issue in the improvement of productivity in shipbuilding plants.A shipyard assembly shop is consisted of pinnedworkplaces, equipment, and overhang cranes. Due to the heavy weight of large assembly block, overhang cranes are used to access any areas over other objects without any hindrance in the assembly shop. The height of cranes can limit the height of blocks that can be assembled in the shop. The shop can be considered as a two-dimensional space. The blocks are placed on precisely pinned workplaces.Once the block is allocated to a certain area in a workplace, it is desirable not to move the block again to different locations due to the size and weight of the large assembly blocks. Therefore, it is important to allocate the workspace to each block carefully, so that the workspace in an assembly shop can be utilized in a most efficient way. In addition, since each block has its due date which is pre-determined at the stage of ship design, the tardiness of a block assembly can lead to severe delay in the following operations. Therefore, in the spatial scheduling problem for large assembly blocks, the scheduling of assembly processes for blocks and the allocation ofblocks to specific locations in workplaces must be considered at the same time. As the terminology suggests, spatial scheduling pursues the optimal spatial layout and the dynamic schedule which can also satisfy traditional scheduling constraints simultaneously. In addition, there are many constraints or requirements which are serious concerns on shop floors and these complicate the SPP. The constraints or requirements this study considered are explained here: (1) Blocks can be put in either directions, horizontal or vertical. (2) Since the ship is symmetric around the centerline, there exist symmetric blocks. These symmetric blocks are required to be put next to each other on the same workplace. (3) Some blocks are required to be put on a certain special area of the workplace, because the work teams on that area has special equipment or skills to achieve a certain level of quality or complete the necessary tasks. (4) Frequently, the production plan may not be implemented as planned, so that frequent modifications in production plansare required to cope with the changes in the shop. At these modifications, it is required to produce a new modified production plan which does not remove or move the pre-existing blocks in the workplace to complete the ongoing operations. (5) If possible at any time, the load balancing over the work teams, i.e., workplaces are desirable in order to keep all task assignments to work teams fair and uniform.Lee, Lee, and Choi (1996) studied a spatial scheduling that considers not only traditional scheduling constraints like resource capacity and due dates, but also dynamic spatial layout of the objects. They used two-dimensional arrangement algorithm developed by Lozano-Perez (1983) to determine the spatial layout of blocks in shipbuilding. Koh, Park, Choi, and Joo (1999) developed a block assembly scheduling system for a shipbuilding company. They proposed a two-phase approach that includes a scheduling phase and a spatial layout phase. Koh, Eom, and Jang (2008) extended their precious works (Koh et al., 1999) byproposing the largest contact area policy to select a better allocation of blocks. Cho, Chung, Park, Park, and Kim (2001) proposed a spatial scheduling system for block painting process in shipbuilding, including block scheduling, four arrangement algorithms and block assignment algorithm. Park et al. (2002) extended Cho et al. (2001) utilizing strategy simulation in two consecutive operations of blasting and painting. Shin, Kwon, and Ryu (2008) proposed a bottom-left-fill heuristic method for spatial planning of block assemblies and suggested a placement algorithm for blocks by differential evolution arrangement algorithm. Liu, Chua, and Wee (2011) proposed a simulation model which enabled multiple priority rules to be compared. Zheng, Jiang, and Chen (2012) proposed a mathematical programming model for spatial scheduling and used several heuristic spatial scheduling strategies (grid searching and genetic algorithm). Zhang and Chen (2012) proposed another mathematical programming model and proposed the agglomeration algorithm.This study presents a novel mixed integer programming (MIP) formulation to consider block rotations, symmetrical blocks, pre-existing blocks, load balancing and allocation of certain blocks topre-determined workspace. The proposed MIP models were implemented by commercially available software, LINGO®and problems of various sizes are tested. The computational results show that the MIP model is extremely difficult to solve as the size of problems grows. To efficiently solve the problem, a two-stage heuristic algorithm has been proposed.Section 2 describes spatial scheduling problems and assumptions which are used in this study. Section 3 presents a mixed integer programming formulation. In Section 4, a two-stage heuristic algorithm has been proposed, including block dispatching priority rules and a diagonal fill space allocation heuristic method, which is modified from the bottom-left-fill space allocation method. Computational results are provided in Section 5. The conclusions are given in Section 6.2. Problem descriptionsThe ship design decides how to divide the ship into many smaller pieces. The metal sheets are cut, blast, bend and weld to build small blocks. These small blocks are assembled to bigger assembly blocks. During this shipbuilding process, all blocks have their earliest starting times which are determined from the previous operational step and due dates which are required by the next operational step. At each step, the blocks have their own shapes of various sizes and handling requirements. During the assembly, no block can overlap physically with others or overhang the boundary of workplace.The spatial scheduling problem can be defined as a problem to determine the optimal schedule of a given set of blocks and the layout of workplaces by designating the blocks’ workplace simultaneously. As the term implies, spatial scheduling pursues the optimal dynamic spatial layout schedule which can also satisfy traditional scheduling constraints. Dynamic spatial layout schedule can be including thespatial allocation issue, temporal allocation issue and resource allocation issue.An example of spatial scheduling is given in Fig. 2. There are 4 blocks to be allocated and scheduled in a rectangular workplace. Each block is shaded in different patterns. Fig. 2 shows the 6-day spatial schedule of four large blocks on a given workplace. Blocks 1 and 2 are pre-existed or allocated at day 1. The earliest starting times of blocks 3 and 4 are days 2 and 4, respectively. The processing times of blocks 1, 2 and 3 are 4, 2 and 4 days, respectively. The spatial schedule must satisfy the time and space constraints at the same time. There are many objectives in spatial scheduling, including the minimization of makespan, the minimum tardiness, the maximum utilization of spatial and non-spatial resources and etc. The objective in this study is to minimize the makespan and balance the workload over the workspaces.There are many constraints for spatial scheduling problems in shipbuilding, depending on the types ofships built, the operational strategies of the shop, organizational restrictions and etc. Some basic constraints are given as follows; (1) all blocks must be allocated on given workplaces for assembly processes and must not overstep the boundary of the workplace; (2) any block cannot overlap with other blocks; (3) all blocks have their own earliest starting time and due dates; (4) symmetrical blocks needs to be placed side-by-side in the same workspace. Fig. 3 shows how symmetrical blocks need to be assigned; (5) some blocks need to be placed in the designated workspace; (6) there can be existing blocks before the planning horizon; (7) workloads for workplaces needs to be balanced as much as possible. In addition to the constraints described above, the following assumptions are made.(1)The shape of blocks and workplaces is rectangular.(2)Once a block is placed in a workplace, it cannot be moved or removed from its location until the process is completed.(3)Blocks can be rotated at angles of 0° and 90° (see Fig. 4).(4)The symmetric blocks have the same sizes, are rotated at the same angle and should be placed side-by-side on the same workplace.(5)The non-spatial resources (such as personnel or equipment) are adequate.3. A mixed integer programming modelA MIP model is formulated and given in this section. The objective function is to minimize makespan and the sum of deviation from average workload per workplace, considering the block rotation, the symmetrical blocks, pre-existing blocks, load balancing and the allocation of certain blocks to pre-determined workspace.A workspace with the length LENW and the width WIDW is considered two-dimensional rectangular space. Since the rectangular shapes for the blocks have been assumed, a block can be placed on workspace by determining (x, y) coordinates, where0 ⩽x ⩽LENW and 0 ⩽y ⩽WIDW. Hence, the dynamic layout of blocks on workplaces is similar to two-dimensional bin packing problem. In addition to the block allocation, the optimal schedule needs to be considered at the same time in spatial scheduling problems. Z axis is introduced to describe the time dimension. Then, spatial scheduling problem becomes a three-dimensional bin packing problem with various objectives and constraints.The decision variables of spatial scheduling problem are (x, y, z) coordinates of all blocks within a three-dimensional space whose sizes are LENW, WIDW and T in x, y and z axes, where T represents the planning horizon. This space is illustrated in Fig. 5.In Fig. 6, the spatial scheduling of two blocks into a workplace is illustrated as an example. The parameters p1 and p2 indicate the processing times for Blocks 1 and 2, respectively. As shown in z axis, Block 2 is scheduled after Block 1 is completed. 4. A two-stage heuristic algorithmThe computational experiments for the MIP model in Section 3 have been conducted using a commercially available solver, LINGO®. Obtaining global optimum solutions is very time consuming, considering the number of variables and constraints. A ship is consisted of more than 8 hundred large blocks and the size of problem using MIP model is beyond today’s computational ability. A two-stage heuristic algorithm has been proposed using the dispatching priority rules and a diagonal fill method.4.1. Stage 1: Load balancing and sequencingPast research on spatial scheduling problems considers various priority rules. Lee et al. (1996) used a priority rule for the minimum slack time of blocks. Cho et al. (2001) and Park et al. (2002) used the earliest due date. Shin et al. (2008) considered three dispatching priority rules for start date, finish date and geometric characteristics (length, breadth, and area) of blocks. Liu and Teng (1999) compared 9 different dispatching priority rules including first-come first-serve, shortestprocessing time, least slack, earliest due date, critical ratio, most waiting time multiplied by tonnage, minimal area residue, and random job selection. Zheng et al. (2012) used a dispatching rule of longest processing time and earliest start time.Two priority rules are used in this study to divide all blocks into groups for load balancing and to sequence them considering the due date and earliest starting time. Two priority rules are streamlined to load-balance and sequence the blocks into an algorithm which is illustrated in Fig. 7. The first step of the algorithm in this stage is to group the blocks based on the urgency priority. The urgency priority is calculated by subtracting the earliest starting time and the processing time from the due date for each block. The smaller the urgency priority, the more urgent the block needs to bed scheduled. Then all blocks are grouped into an appropriate number of groups for a reasonable number of levels in urgency priorities. Let g be this discretionary number ofgroups. There are g groups of blocks based on the urgency of blocks. The number of blocks in each group does not need to be identical.Blocks in each group are re-ordered grouped into as many subgroups as workplaces, considering the workload of blocks such as the weight or welding length. The blocks in each subgroup have the similar urgency and workloads. Then, these blocks in each subgroup are ordered in an ascending order of the earliest starting time. This ordering will be used to block allocations in sequence. The subgroup corresponds to the workplace.If block i must be processed at workplace w and is currently allocated to other workplace or subgroup than w, block i is swapped with a block at the same position of block i in an ascending order of the earliest starting time at its workplace (or subgroup). Since the symmetric blocks must be located on a same workplace, a similar swapping method can be used. One of symmetric blocks which are allocated into different workplace (or subgroups) needs to beselected first. In this study, we selected one of symmetric blocks whichever has shown up earlier in an ascending order of the earliest starting time at their corresponding workplace (or subgroup). Then, the selected block is swapped with a block at the same position of symmetric blocks in an ascending order of the earliest starting time at its workplace (or subgroups).4.2. Stage 2: Spatial allocationOnce the blocks in a workplace (or subgroup) are sequentially ordered in different urgency priority groups, each block can be assigned to workplaces one by one, and allocated to a specific location on a workplace. There has been previous research on heuristic placement methods. The bottom-left (BL) placement method was proposed by Baker, Coffman, and Rivest (1980) and places rectangles sequentially in a bottom-left most position. Jakobs (1996) used a bottom-left method that is combined with a hybrid genetic algorithm (see Fig. 8). Liu and Teng (1999) developed an extended bottom-left heuristic whichgives priority to downward movement, where the rectangles is only slide leftwards if no downward movement is possible. Chazele (1983) proposed the bottom-left-fill (BLF) method, which searches for lowest bottom-left point, holes at the lowest bottom-left point and then place the rectangle sequentially in that bottom-left position. If the rectangle is not overlapped, the rectangle is placed and the point list is updated to indicate new placement positions. If the rectangle is overlapped, the next point in the point list is selected until the rectangle can be placed without any overlap. Hopper and Turton (2000) made a comparison between the BL and BLF methods. They concluded that the BLF method algorithm achieves better assignment patterns than the BL method for Hopper’s example problems. Spatial allocation in shipbuilding is different from two-dimensional packing problem. Blocks have irregular polygonal shapes in the spatial allocation and blocks continuously appear and disappear since they have their processing times. This frequentplacement and removal of blocks makes BLF method less effective in spatial allocation of large assembly block.In order to solve these drawbacks, we have modified the BLF method appropriate to spatial scheduling for large assembly blocks. In a workplace, since the blocks are placed and removed continuously, it is more efficient to consider both the bottom-left and top-right points of placed blocks instead of bottom-left points only. We denote it as diagonal fill placement (see Fig. 9). Since the number of potential placement considerations increases, it takes a bit more time to implement diagonal fill but the computational results shows that it is negligible.The diagonal fill method shows better performances than the BLF method in spatial scheduling problems. When the BLF method is used in spatial allocation, the algorithm makes the allocation of some blocks delayed until the interference by pre-positioned blocks are removed. It generates a less effective andless efficient spatial schedule. The proposed diagonal fill placement method resolve this delays better by allocating the blocks as soon as possible in a greedy way, as shown in Fig. 10. The potential drawbacks from the greedy approaches is resolved by another placement strategy to minimize the possible dead spaces, which will be explained in the following paragraphs.The BLF method only focused on two-dimensional bin packing. Frequent removal and placement of blocks in a workspace may lead to accumulation of dead spaces, which are small and unusable spaces among blocks. A minimal possible-dead space strategy has been used along with the BLF method. Possible-dead spaces are being generated over the spatial scheduling and they have less chance to be allocated for future blocks. The minimal possible-dead space strategy minimizes the potential dead space after allocating the following blocks (Chung, 2001 and Koh et al., 2008) by considering the 0° and 90° rotation of the block and allocating the following block for minimalpossible-dead space. Fig. 11 shows an example of three possible-dead space calculations using the neighbor block search method. When a new scheduling block is considered to be allocated, the rectangular boundary of neighboring blocks and the scheduling blocks is searched. This boundary can be calculated by obtaining the smallest and the largest x and y coordinates of neighboring blocks and the scheduling blocks. Through this procedure, the possible-dead space can be calculated as shown in Fig. 11. Considering the rotation of the scheduling blocks and the placement consideration points from the diagonal fill placement methods, the scheduling blocks will be finally allocated.In this two-stage algorithm, blocks tend to be placed adjacent to one of the alternative edges and their assignments are done preferentially to minimize fractured spaces.5. Computational resultsTo demonstrate the effectiveness and efficiency of the proposed MIP formulation and heuristic algorithm,the actual data about 800+ large assembly blocks from one of major shipbuilding companies has been obtained and used. All test problems are generated from this real-world data.All computational experiments have been carried out on a personal computer with a Intel®Core™i3-2100 CPU @ 3.10 GHz with 2 GB RAM. The MIP model in Section 3 has been programmed and solved using LINGO® version 10.0, a commercially available software which can solve linear and nonlinear models. The proposed two-stage heuristic algorithm has been programmed in JAVA programming language.Because our computational efforts to obtain the optimal solutions for even small problems are more than significant, the complexity of SPP can be recognized as one of most difficult and time consuming problems.Depending on the scaling factor α in objective function of the proposed MIP formulation, the performance of the MIP model varies significantly. Setting α less than 0.01 makes the load balancingcapability to be ignored from the optimal solution in the MIP model. For computational experiments in this study, the results with the scaling factor set to 0.01 is shown and discussed. The value needs to be fine-turned to obtain the desirable outcomes. Table 1 shows a comparison of computational results and performance between the MIP models and two-stage heuristic algorithm. As shown in Table 1, the proposed two-stage heuristic algorithm finds the near-optimal solutions for medium and large problems very quickly while the optimal MIP models was not able to solve the problems of medium or large sizes due to the memory shortage on computers. It is observed that the computational times for the MIP problems are rapidly growing as the problem sizes increases. The test problems in Table 1 have 2 workplaces.Table 1.Computational results and performance between the MIP models and two-stage heuristic algorithm.The MIP model Two-stage heuristic algorithm Number of blocksOptimal solution Time (s) Best known solution Time (s)10 12.360 1014.000 12.360 0.02620 22.380a 38250.000 21.380 0.07830 98.344a 38255.000 30.740 0.21850 ––53.760 0.719100 ––133.780 2.948200 ––328.860 12.523300 ––416.060 40.154400 ––532.360 73.214 Best feasible solution after 10 h in Global Solver of LINGO®.Full-size tableTable optionsView in workspaceDownload as CSVThe optimal solutions for test problems with more than 50 blocks in Table 1 have been not obtained even after 24 h. The best known feasible solutions after 10 h for the test problems with 20 blocks and 30 blocks are reported in Table 1. It is observed that the LINGO®does not solve the nonlinear constraints very well as shown in Table 1. For very small problemwith 10 blocks, the LINGO® was able to achieve the optimal solutions. For slightly bigger problems, the LINGO®took significantly more time to find feasible solutions. From this observation, the approaches to obtain the lower bound through the relaxation method and upper bounds are significant required in future research.In contrary, the proposed two-stage heuristic algorithm was able to find the good solutions very quickly. For the smallest test problem with 10 blocks, it was able to find the optimal solution as well. The computational times are 1014 and 0.026 s, respectively, for the MIP approach and the proposed algorithm. Interestingly, the proposed heuristic algorithm found significantly better solutions in only 0.078 and 0.218 s, respectively, for the test problems with 20 and 30 blocks. For these two problems, the LINGO® generates the worse solutions than the heuristics after 10 h of computational times. The symbol ‘–’ in Table 1 indicates that the Global Solver of LINGO® did not find the feasiblesolutions.Another observation on the two-stage heuristic algorithms is the robust computational times. The computation times does not change much as the problem sizes increase. It is because the simple priority rules are used without considering many combinatorial configurations.Fig. 12 shows partial solutions of test problems with 20 and 30 blocks on 2 workplaces. The purpose of Fig.12 is to show the progress of production planning generated by the two-stage heuristic algorithm. Two workplaces are in different sizes of (40, 30) and (35, 40), respectively.6. ConclusionsAs global warming is expected to open a new way to transport among continent through North Pole Sea and to expedite the oceans more aggressively, the needs for more ships and ocean plants are forthcoming. The shipbuilding industries currently face increased diversity of assembly blocks in limited productionshipyard. Spatial scheduling for large assembly blocks holds the key role in successful operations of the shipbuilding companies.The task of spatial scheduling takes place at almost every stage of shipbuilding processes and the large assembly shop is one of the most congested operational areas in today’s shipbuilding. It is also known that the spatial scheduling problem has been the major source of the bottleneck. The practitioners in shipbuilding industries requires their production planning system to optimize the spatial scheduling and to respond quickly to the changes on the shop floor by re-optimizing the production plan in 20-min time frame. Most companies use a system employing heuristic methods in an ad-hoc manner without knowing how good their planning system is.To benchmark the performance of the heuristic algorithms, a novel MIP model has been proposed considering various real-world constraints that are raised by field professionals and engineers. Those。

船舶专业外文文献之欧阳与创编

Spatial scheduling for largeassemblyblocks inshipbuildingAbstract: This paper addresses the spatial scheduling problem (SPP) for large assembly blocks, which arises in a shipyard assembly shop. The spatial scheduling problem is to schedule a set of jobs, of which each requires its physical space in a restricted space. This problem is complicated because both the scheduling of assemblies with different due dates and earliest starting times and the spatial allocation of blocks with different sizes and loads must be considered simultaneously. This problem under consideration aims to the minimization of both the makespan and the load balance and includes variousreal-world constraints, which includes the possible directional rotation of blocks, the existence of symmetric blocks, and the assignment of some blocks to designatedworkplaces or work teams. The problem is formulated as a mixed integer programming (MIP) model and solved by a commercially available solver. A two-stage heuristic algorithm has been developed to use dispatching priority rules and a diagonal fill space allocation method, which is a modification of bottom-left-fill space allocation method. The comparison and computational results shows the proposed MIP model accommodates various constraints and the proposed heuristic algorithm solves the spatial scheduling problems effectively and efficiently. Keywords:Large assembly block; Spatial scheduling; Load balancing; Makespan; Shipbuilding1. IntroductionShipbuilding is a complex production process characterized by heavy and large parts, various equipment, skilled professionals, prolonged lead time, and heterogeneous resource requirements. The shipbuilding process is divided into sub processes in the shipyard, including ship design, cutting and bending operations, block assembly, outfitting, painting, pre-erection anderection. The assembly blocks are called the minor assembly block, the sub assembly block, and the large assembly block according to their size and progresses in the course of assembly processes. This paper focuses on the spatial scheduling problem of large assembly blocks in assembly shops. Fig. 1 shows a snapshot of large assembly blocks in a shipyard assembly shop.Recently, the researchers and practitioners at academia and shipbuilding industries recently got together at “Smart Production Technology Forum in Shipbuilding and Ocean Plant Industries” to recognize that there are various spatial scheduling problems in every aspect of shipbuilding due to the limited space, facilities, equipment, labor and time. The SPPs occur in various working areas such as cutting and blast shops, assembly shops, outfitting shops, pre-erection yard, and dry docks. The SPP at different areas has different requirements and constraints to characterize the unique SPPs. In addition, the depletion of energy resources on land put more emphasis on the ocean development. The shipbuilding industries face the transition of focus from thetraditional shipbuilding to ocean plant manufacturing. Therefore, the diversity of assembly blocks, materials, facilities and operations in ship yards increases rapidly. There are some solution providers such as Siem ens™ and Dassult Systems™ to provide integrated software including product life management, enterprise resource planning system, simulation and etc. They indicated the needs of efficient algorithms to solve medium- tolarge-sized SPP problems in 20 min, so that the shop can quickly re-optimize the production plan upon the frequent and unexpected changes in shop floors with the ongoing operations on exiting blocks intact.There are many different applications which require efficient scheduling algorithms with various constraints and characteristics (Kim and Moon, 2003, Kim et al., 2013, Nguyen and Yun, 2014 and Yan et al., 2014). However, the spatial scheduling problem which considers spatial layout and dynamic job scheduling has not been studied extensively. Until now, spatial scheduling has to be carried out by human schedulers only with their experiences andhistorical data. Even when human experts have much experience in spatial scheduling, it takes a long time and intensive effort to produce a satisfactory schedule, due to the complexity of considering blocks’ geometric shapes, loads, required facilities, etc. In practice, spatial scheduling for more than a six-month period is beyond the human schedulers’ capacity. Moreover, the space in the working areas tends to be the most critical resource in shipbuilding. Therefore, the effective management of spatial resources through automation of the spatial scheduling process is a critical issue in the improvement of productivity in shipbuilding plants.A shipyard assembly shop is consisted of pinned workplaces, equipment, and overhang cranes. Due to the heavy weight of large assembly block, overhang cranes are used to access any areas over other objects without any hindrance in the assembly shop. The height of cranes can limit the height of blocks that can be assembled in the shop. The shop can be considered as a two-dimensional space. The blocks are placed on precisely pinned workplaces.Once the block is allocated to a certain area in a workplace, it is desirable not to move the block again to different locations due to the size and weight of the large assembly blocks. Therefore, it is important to allocate the workspace to each block carefully, so that the workspace in an assembly shop can be utilized in a most efficient way. In addition, since each block has its due date which ispre-determined at the stage of ship design, the tardiness of a block assembly can lead to severe delay in the following operations. Therefore, in the spatial scheduling problem for large assembly blocks, the scheduling of assembly processes for blocks and the allocation of blocks to specific locations in workplaces must be considered at the same time. As the terminology suggests, spatial scheduling pursues the optimal spatial layout and the dynamic schedule which can also satisfy traditional scheduling constraints simultaneously. In addition, there are many constraints or requirements which are serious concerns on shop floors and these complicate the SPP. The constraints or requirements this study considered are explained here: (1) Blocks can be put in either directions, horizontal orvertical. (2) Since the ship is symmetric around the centerline, there exist symmetric blocks. These symmetric blocks are required to be put next to each other on the same workplace. (3) Some blocks are required to be put on a certain special area of the workplace, because the work teams on that area has special equipment or skills to achieve a certain level of quality or complete the necessary tasks. (4) Frequently, the production plan may not be implemented as planned, so that frequent modifications in production plans are required to cope with the changes in the shop. At these modifications, it is required to produce a new modified production plan which does not remove or move the pre-existing blocks in the workplace to complete the ongoing operations. (5) If possible at any time, the load balancing over the work teams, i.e., workplaces are desirable in order to keep all task assignments to work teams fair and uniform.Lee, Lee, and Choi (1996) studied a spatial scheduling that considers not only traditional scheduling constraints like resource capacity and due dates, but also dynamic spatiallayout of the objects. They used two-dimensional arrangement algorithm developed by Lozano-Perez (1983) to determine the spatial layout of blocks in shipbuilding. Koh, Park, Choi, and Joo (1999) developed a block assembly scheduling system for a shipbuilding company. They proposed a two-phase approach that includes a scheduling phase and a spatial layout phase. Koh, Eom, and Jang (2008) extended their precious works (Koh et al., 1999) by proposing the largest contact area policy to select a better allocation of blocks. Cho, Chung, Park, Park, and Kim (2001) proposed a spatial scheduling system for block painting process in shipbuilding, including block scheduling, four arrangement algorithms and block assignment algorithm. Park et al. (2002) extended Cho et al. (2001) utilizing strategy simulation in two consecutive operations of blasting and painting. Shin, Kwon, and Ryu (2008) proposed a bottom-left-fill heuristic method for spatial planning of block assemblies and suggested a placement algorithm for blocks by differential evolution arrangement algorithm. Liu, Chua, and Wee (2011) proposed a simulation model which enabled multiplepriority rules to be compared. Zheng, Jiang, and Chen (2012) proposed a mathematical programming model for spatial scheduling and used several heuristic spatial scheduling strategies (grid searching and genetic algorithm). Zhang and Chen (2012) proposed another mathematical programming model and proposed the agglomeration algorithm.This study presents a novel mixed integer programming (MIP) formulation to consider block rotations, symmetrical blocks, pre-existing blocks, load balancing and allocation of certain blocks to pre-determined workspace. The proposed MIP models were implemented by commercially available software, LINGO® and problems of various sizes are tested. The computational results show that the MIP model is extremely difficult to solve as the size of problems grows. To efficiently solve the problem, a two-stage heuristic algorithm has been proposed.Section 2 describes spatial scheduling problems and assumptions which are used in this study. Section 3presents a mixed integer programming formulation. In Section 4, a two-stage heuristic algorithm has been proposed, including block dispatching priority rules and a diagonal fill space allocation heuristic method, which is modified from the bottom-left-fill space allocation method. Computational results are provided in Section 5. The conclusions are given in Section 6.2. Problem descriptionsThe ship design decides how to divide the ship into many smaller pieces. The metal sheets are cut, blast, bend and weld to build small blocks. These small blocks are assembled to bigger assembly blocks. During this shipbuilding process, all blocks have their earliest starting times which are determined from the previous operational step and due dates which are required by the next operational step. At each step, the blocks have their own shapes of various sizes and handling requirements. During the assembly, no block can overlap physically with others or overhang the boundary of workplace.The spatial scheduling problem can be defined as aproblem to determine the optimal schedule of a given set of blocks and the layout of workplaces by designating the blocks’ workplace simultaneously. As the term implies, spatial scheduling pursues the optimal dynamic spatial layout schedule which can also satisfy traditional scheduling constraints. Dynamic spatial layout schedule can be including the spatial allocation issue, temporal allocation issue and resource allocation issue.An example of spatial scheduling is given in Fig. 2. There are 4 blocks to be allocated and scheduled in a rectangular workplace. Each block is shaded in different patterns. Fig.2 shows the 6-day spatial schedule of four large blocks on a given workplace. Blocks 1 and 2 are pre-existed or allocated at day 1. The earliest starting times of blocks3 and4 are days 2 and 4, respectively. The processing times of blocks 1, 2 and 3 are 4, 2 and 4 days, respectively.The spatial schedule must satisfy the time and space constraints at the same time. There are many objectives in spatial scheduling, including the minimization of makespan, the minimum tardiness, the maximumutilization of spatial and non-spatial resources and etc. The objective in this study is to minimize the makespan and balance the workload over the workspaces.There are many constraints for spatial scheduling problems in shipbuilding, depending on the types of ships built, the operational strategies of the shop, organizational restrictions and etc. Some basic constraints are given as follows; (1) all blocks must be allocated on given workplaces for assembly processes and must not overstep the boundary of the workplace; (2) any block cannot overlap with other blocks; (3) all blocks have their own earliest starting time and due dates; (4) symmetrical blocks needs to be placed side-by-side in the same workspace. Fig.3 shows how symmetrical blocks need to be assigned; (5) some blocks need to be placed in the designated workspace;(6) there can be existing blocks before the planning horizon;(7) workloads for workplaces needs to be balanced as much as possible.In addition to the constraints described above, the following assumptions are made.(1)The shape of blocks and workplaces is rectangular.(2)Once a block is placed in a workplace, it cannot be moved or removed from its location until the process is completed.(3)Blocks can be rotated at angles of 0° and 90° (see Fig.4).(4)The symmetric blocks have the same sizes, are rotated at the same angle and should be placed side-by-side on the same workplace.(5)The non-spatial resources (such as personnel or equipment) are adequate.3. A mixed integer programming modelA MIP model is formulated and given in this section. The objective function is to minimize makespan and the sum of deviation from average workload per workplace, considering the block rotation, the symmetrical blocks, pre-existing blocks, load balancing and the allocation of certain blocks to pre-determined workspace.A workspace with the length LENW and the width WIDWis considered two-dimensional rectangular space. Since the rectangular shapes for the blocks have been assumed, a block can be placed on workspace by determining (x, y) coordinates, where 0 ⩽ x ⩽ LENW and 0 ⩽ y ⩽ WIDW. Hence, the dynamic layout of blocks on workplaces is similar to two-dimensional bin packing problem. In addition to the block allocation, the optimal schedule needs to be considered at the same time in spatial scheduling problems. Z axis is introduced to describe the time dimension. Then, spatial scheduling problem becomes a three-dimensional bin packing problem with various objectives and constraints.The decision variables of spatial scheduling problem are (x, y, z) coordinates of all blocks within a three-dimensional space whose sizes are LENW, WIDW and T in x, y and z axes, where T represents the planning horizon. This space is illustrated in Fig. 5.In Fig. 6, the spatial scheduling of two blocks into a workplace is illustrated as an example. The parameters p1 and p2 indicate the processing times for Blocks 1 and 2,respectively. As shown in z axis, Block 2 is scheduled after Block 1 is completed.4. A two-stage heuristic algorithmThe computational experiments for the MIP model in Section 3 have been conducted using a commercially available solver, LINGO®. Obtaining global optimum solutions is very time consuming, considering the number of variables and constraints. A ship is consisted of more than 8 hundred large blocks and the size of problem using MIP model is beyond today’s computational ability. A two-stage heuristic algorithm has been proposed using the dispatching priority rules and a diagonal fill method.4.1. Stage 1: Load balancing and sequencingPast research on spatial scheduling problems considers various priority rules. Lee et al. (1996) used a priority rule for the minimum slack time of blocks. Cho et al. (2001) and Park et al. (2002) used the earliest due date. Shin et al. (2008) considered three dispatching priority rules for start date, finish date and geometric characteristics (length, breadth, and area) of blocks. Liu and Teng (1999)compared 9 different dispatching priority rules including first-come first-serve, shortest processing time, least slack, earliest due date, critical ratio, most waiting time multiplied by tonnage, minimal area residue, and random job selection. Zheng et al. (2012) used a dispatching rule of longest processing time and earliest start time.Two priority rules are used in this study to divide all blocks into groups for load balancing and to sequence them considering the due date and earliest starting time. Two priority rules are streamlined to load-balance and sequence the blocks into an algorithm which is illustrated in Fig. 7. The first step of the algorithm in this stage is to group the blocks based on the urgency priority. The urgency priority is calculated by subtracting the earliest starting time and the processing time from the due date for each block. The smaller the urgency priority, the more urgent the block needs to bed scheduled. Then all blocks are grouped into an appropriate number of groups for a reasonable number of levels in urgency priorities. Let g be this discretionary number of groups. There are g groups of blocks based onthe urgency of blocks. The number of blocks in each group does not need to be identical.Blocks in each group are re-ordered grouped into as many subgroups as workplaces, considering the workload of blocks such as the weight or welding length. The blocks in each subgroup have the similar urgency and workloads. Then, these blocks in each subgroup are ordered in an ascending order of the earliest starting time. This ordering will be used to block allocations in sequence. The subgroup corresponds to the workplace.If block i must be processed at workplace w and is currently allocated to other workplace or subgroup than w, block i is swapped with a block at the same position of block i in an ascending order of the earliest starting time at its workplace (or subgroup). Since the symmetric blocks must be located on a same workplace, a similar swapping method can be used. One of symmetric blocks which are allocated into different workplace (or subgroups) needs to be selected first. In this study, we selected one of symmetric blocks whichever has shown up earlier in anascending order of the earliest starting time at their corresponding workplace (or subgroup). Then, the selected block is swapped with a block at the same position of symmetric blocks in an ascending order of the earliest starting time at its workplace (or subgroups).4.2. Stage 2: Spatial allocationOnce the blocks in a workplace (or subgroup) are sequentially ordered in different urgency priority groups, each block can be assigned to workplaces one by one, and allocated to a specific location on a workplace. There has been previous research on heuristic placement methods. The bottom-left (BL) placement method was proposed by Baker, Coffman, and Rivest (1980) and places rectangles sequentially in a bottom-left most position. Jakobs (1996) used a bottom-left method that is combined with a hybrid genetic algorithm (see Fig. 8). Liu and Teng (1999) developed an extended bottom-left heuristic which gives priority to downward movement, where the rectangles is only slide leftwards if no downward movement is possible. Chazele (1983) proposed the bottom-left-fill (BLF)method, which searches for lowest bottom-left point, holes at the lowest bottom-left point and then place the rectangle sequentially in that bottom-left position. If the rectangle is not overlapped, the rectangle is placed and the point list is updated to indicate new placement positions. If the rectangle is overlapped, the next point in the point list is selected until the rectangle can be placed without any overlap. Hopper and Turton (2000) made a comparison between the BL and BLF methods. They concluded that the BLF method algorithm achieves better assignment patterns than the BL method for Hopper’s example problems. Spatial allocation in shipbuilding is different fromtwo-dimensional packing problem. Blocks have irregular polygonal shapes in the spatial allocation and blocks continuously appear and disappear since they have their processing times. This frequent placement and removal of blocks makes BLF method less effective in spatial allocation of large assembly block.In order to solve these drawbacks, we have modified the BLF method appropriate to spatial scheduling for largeassembly blocks. In a workplace, since the blocks are placed and removed continuously, it is more efficient to consider both the bottom-left and top-right points of placed blocks instead of bottom-left points only. We denote it as diagonal fill placement (see Fig. 9). Since the number of potential placement considerations increases, it takes a bit more time to implement diagonal fill but the computational results shows that it is negligible.The diagonal fill method shows better performances than the BLF method in spatial scheduling problems. When the BLF method is used in spatial allocation, the algorithm makes the allocation of some blocks delayed until the interference by pre-positioned blocks are removed. It generates a less effective and less efficient spatial schedule. The proposed diagonal fill placement method resolve this delays better by allocating the blocks as soon as possible in a greedy way, as shown in Fig. 10. The potential drawbacks from the greedy approaches is resolved by another placement strategy to minimize the possible dead spaces, which will be explained in the following paragraphs.The BLF method only focused on two-dimensional bin packing. Frequent removal and placement of blocks in a workspace may lead to accumulation of dead spaces, which are small and unusable spaces among blocks. A minimal possible-dead space strategy has been used along with the BLF method. Possible-dead spaces are being generated over the spatial scheduling and they have less chance to be allocated for future blocks. The minimal possible-dead space strategy minimizes the potential dead space after allocating the following blocks (Chung,2001 and Koh et al., 2008) by considering the 0° and 90°rotation of the block and allocating the following block for minimal possible-dead space. Fig. 11 shows an example of three possible-dead space calculations using the neighbor block search method. When a new scheduling block is considered to be allocated, the rectangular boundary of neighboring blocks and the scheduling blocks is searched. This boundary can be calculated by obtaining the smallest and the largest x and y coordinates of neighboring blocks and the scheduling blocks. Through this procedure, the possible-dead space can be calculated as shown in Fig. 11.Considering the rotation of the scheduling blocks and the placement consideration points from the diagonal fill placement methods, the scheduling blocks will be finally allocated.In this two-stage algorithm, blocks tend to be placed adjacent to one of the alternative edges and their assignments are done preferentially to minimize fractured spaces.5. Computational resultsTo demonstrate the effectiveness and efficiency of the proposed MIP formulation and heuristic algorithm, the actual data about 800+ large assembly blocks from one of major shipbuilding companies has been obtained and used. All test problems are generated from this real-world data. All computational experiments have been carried out on a personal computer with a Intel® Core™ i3-2100 CPU @ 3.10 GHz with 2 GB RAM. The MIP model in Section 3 has been programmed and solved using LINGO® version 10.0, a commercially available software which can solve linear and nonlinear models. The proposed two-stageheuristic algorithm has been programmed in JA V A programming language.Because our computational efforts to obtain the optimal solutions for even small problems are more than significant, the complexity of SPP can be recognized as one of most difficult and time consuming problems.Depending on the scaling factor α in objective function of the proposed MIP formulation, the performance of the MIP model varies significantly. Setting α less than 0.01 makes the load balancing capability to be ignored from the optimal solution in the MIP model. For computational experiments in this study, the results with the scaling factor set to 0.01 is shown and discussed. The value needs to be fine-turned to obtain the desirable outcomes.Table 1 shows a comparison of computational results and performance between the MIP models and two-stage heuristic algorithm. As shown in Table 1, the proposed two-stage heuristic algorithm finds the near-optimal solutions for medium and large problems very quickly while the optimal MIP models was not able to solve theproblems of medium or large sizes due to the memory shortage on computers. It is observed that the computational times for the MIP problems are rapidly growing as the problem sizes increases. The test problems in Table 1 have 2 workplaces.Table 1.Computational results and performance between the MIP models and two-stage heuristic algorithm.Number of blocks The MIP modelTwo-stage heuristicalgorithmOptimalsolutionTime (s)Best knownsolutionTime (s)10 12.360 1014.000 12.360 0.02620 22.380a 38250.000 21.380 0.07830 98.344a 38255.000 30.740 0.21850 ––53.760 0.719100 ––133.780 2.948200 ––328.860 12.523300 ––416.060 40.154400 ––532.360 73.214 Best feasible solution after 10 h in Global Solver of LINGO®.Full-size tableTable optionsView in workspaceDownload as CSVThe optimal solutions for test problems with more than 50 blocks in Table 1 have been not obtained even after 24 h. The best known feasible solutions after 10 h for the test problems with 20 blocks and 30 blocks are reported in Table 1. It is observed that the LINGO® does not solve the nonlinear constraints very well as shown in Table 1. For very small problem with 10 blocks, the LINGO® was able to achieve the optimal solutions. For slightly bigger problems, the LINGO® took significantly more time to find feasible solutions. From this observation, the approaches to obtain the lower bound through the relaxation method and upper bounds are significant required in future research.In contrary, the proposed two-stage heuristic algorithm was able to find the good solutions very quickly. For thesmallest test problem with 10 blocks, it was able to find the optimal solution as well. The computational times are 1014 and 0.026 s, respectively, for the MIP approach and the proposed algorithm. Interestingly, the proposed heuristic algorithm found significantly better solutions in only 0.078 and 0.218 s, respectively, for the test problems with 20 and 30 blocks. For these two problems, the LINGO® generates the worse solutions than the heuristics after 10 h of computational times. T he symbol ‘–’ in Table 1 indicates that the Global Solver of LINGO® did not find the feasible solutions.Another observation on the two-stage heuristic algorithms is the robust computational times. The computation times does not change much as the problem sizes increase. It is because the simple priority rules are used without considering many combinatorial configurations.Fig. 12 shows partial solutions of test problems with 20 and 30 blocks on 2 workplaces. The purpose of Fig. 12 is to show the progress of production planning generated by the two-stage heuristic algorithm. Two workplaces are indifferent sizes of (40, 30) and (35, 40), respectively.6. ConclusionsAs global warming is expected to open a new way to transport among continent through North Pole Sea and to expedite the oceans more aggressively, the needs for more ships and ocean plants are forthcoming. The shipbuilding industries currently face increased diversity of assembly blocks in limited production shipyard. Spatial scheduling for large assembly blocks holds the key role in successful operations of the shipbuilding companies.The task of spatial scheduling takes place at almost every stage of shipbuilding processes and the large assembly shop is one of the most congested operational areas in today’s shipbuilding. It is also known that the spatial scheduling problem has been the major source of the bottleneck. The practitioners in shipbuilding industries requires their production planning system to optimize the spatial scheduling and to respond quickly to the changes on the shop floor by re-optimizing the production plan in20-min time frame. Most companies use a system。

1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。

一种评估集装箱船结构扭转强度的实用方法K. Iijima, T. Shigemi, R. Miyake_, A. Kumano日本海事协会研究所（NK）摘要集装箱船结构的特点是舱口开口大。

由于这种结构特性，波中复杂的扭力矩影响会引起舱口开口的巨大角变形和翘曲压力。

这就需要在集装箱船的结构设计阶段评估船体梁的扭转强度。

本文在最新分析结果的基础上讨论了一种评估集装箱船结构扭转强度的实用方法。

为了尽可能准确估计扭响应特性，采用在油轮试验中已得到确认的三维兰金源法估计集装箱船的波载荷，并以此用有限元法分析整船模型。

另外，指定集装箱船扭转反应达到最大时的主导规则波条件。

扭转强度评估设计使用荷载，其产生的扭转反应等于长期预测值，并以指定主导波条件下的几个集装箱船的扭力矩为基础检测设计荷载。

同时讨论了用于估计总船体梁应力的一个适当的应力分量组合。

【关键词】：集装箱船扭转强度兰金源法水池试验设计荷载组合应力1.绪论受经济规模影响，集装箱船的体积正在加大。

最近几年集装箱船的发展似乎在加速。

10年前装载容量最大的5500标箱级集装箱船已多少成为目前的标准。

现今，最大的超巴拿马型集装箱船拥有超过8000标箱的容量，甚至12500标箱级集装箱船的基础研究设计已经开始。

[1]Payer[2]讨论了集装箱船在技术和经济方面的发展和转变。

论及集装箱船的顶级技术难题莫过于船体梁的扭转强度，即指，以大舱口开口为特点的集装箱船在波中会受到相当大的扭转变形和翘曲压力。

在这方面，Sun and Soares[3]开创性的研究了带有大舱口开口的船体承受扭力矩的极限强度。

翘曲应力分量应与其他应力分量（例如纵向弯曲应力和横向弯曲应力）一起纳入船体强度评估的考虑范围。

扭转变形可能在舱口角引起集中压力，因此舱口角的设计应考虑到疲劳强度。

众多关于集装箱船结构的研究从构造和流体力学两个方面分析了扭转强度，例如，[4–10]，Shimizu [5,8]用一个梁变截面模拟集装箱船的结构，进行了流体力学和构造分析。

Nakata [6] and Umezaki [7]分别开发了一个总系统，在该系统中船舶运动分析和结构分析与统计分析相结合。

扭转变形受到扭力矩分布和船舶结构刚性参数分布的影响，而纵向或横向弯曲应力只决定于影响该部分剖面模数的相关位置的弯力矩振幅。

接着分析扭转响应时需要总船模型。

再者，扭力矩分析的准确性不仅与规模有关也与分布有关。

正如纵向弯曲应力在不同阶段具有差异，横向弯曲应力和翘曲应力也对总船体梁应力的估计有重大影响。

同时还要求分析复杂的波浪荷载。

因此，数值波浪荷载分析和有限元分析已被用于开发新的集装箱船以及创新设计的集装箱船。

从这个意义上说，集装箱船结构的发展主要依赖于这些数值分析。

另一方面，不能否认的是，船级社为船体梁结构强度评估制定的设计荷载已成为集装箱船设计的标准荷载，应用方便。

这就是说扭转响应估计的准确性、最后的结构标注以及船体的安全性，很大程度上都取决于设计荷载的精确性。

因此，需要发展用于评估扭转变形的设计荷载，以反映最新数值分析得到的准确结果。

利用这个设计荷载在不进行复杂的波浪荷载分析时就能得到更准确的集装箱船体扭曲强度评价，并推进船舶的结构安全。

为了使设计荷载数据可靠，让船舶设计师信服，其设计过程就应该透明和合理。

在一份合著[11]中提出，已经成功开发了针对油轮和货轮主要结构构件的设计荷载估算方法，这些船舶运营商都有透明和一致的背景。

相关资料讨论了设计海况、设计波和设计荷载之间的关系，最后结果表明以下方法得到的设计荷载可使所得响应可能等同于长期预测响应值。

目前，我们的目标是得到一个切实可行的方法，就是在尽可能准确分析得到的结果的基础上评估透明和一致背景下的扭转强度。

主要讨论了评估集装箱船船体梁扭转强度所需的设计荷载和最佳应力分量组合。

研究步骤如下：⑴建立波荷载估算方法：尽管有几例实验研究了集装箱船的扭力矩，但似乎无一针对超巴拿马型集装箱船。

水池试验中第一次得到验证的数值分析法在一份合著中有了发展。

⑵精确结构分析：第一个计算波浪中集装箱船的扭力矩，然后将荷载直接应用于整船的有限元模型。

确定翘曲压力和相对变形的响应函数，同时确定短期和长期（超越概率Q=10-8）预测值。

⑶提出和校正设计荷载：参照响应函数指定主导规则波条件下集装箱船的最大扭转变形。

计算10艘不同大小的集装箱船在主导波条件下的扭力矩，以该结果为依据制定设计荷载，并将其所得响应与步骤⑵中得到的长期预测值进行比较。

⑷提出和校正应力分量组合：步骤⑵后计算纵向应力分量、横向应力分量和总船梁应力的响应函数，严格审查不同阶段的应力分量。

参照响应函数之间的关系提出最佳应力分量组合，依此得到总船梁压力。

将所得总船梁压力与长期预测值进行比较，并严格审查不同阶段的应力分量。

⑸基于上述提出评估扭转强度的实用方法。

2.波荷载估算2.1 各种数值分析方法的优缺点多种带状法[12,13]已被开发和运用于估算波浪引起的船舶运动和包括纵向弯曲力矩和横向弯曲力矩在内的波荷载，该法有足够的准确度，实际应用性强。

带状法作为一个标准工具被广泛应用于估算非线性荷载和运动14，现在有时也估算船体结构的弹性15-17。

但是，因为它们没有准确考虑纵向带之间反射波的水动力干扰效应和立体效应，在短波估算时其准确性值得怀疑。

为了提高估算的准确性，尤其在短波条件下的准确性，已经提出许过考虑三维效应的数值分析方法。

其中包括基于三维势流理论提出的三维Green函数法18和兰金源法19-21。

这些方法的优势在于考虑三维效应，有良好的计算稳定性和适中的计算时间。

因此，它们有望作为方便的设计工具而取代带状法。

尽管大部分三维法最初是为了频域仿真，但它们很快发展为分析方法，与同时提升的计算机能力一起为时域仿真22-23效力。

这促进了非线性效应在时域仿真上的应用，同时出现的还有波浪振幅限度以及可能对设计荷载估算有重大影响的船舶运动。

这些方法的缺点之一是仍然需要耗费大量的计算时间。

一份合著显示，计算流体力学（CFD）可更准确的估算涉及波浪限度和运动振幅的波荷载。

自从改法直接从数值上解决了Navier-stokes方程，即使是高度非线性现象，如抨击和绿水航运都可纳入考虑范围。

尽管这种方法不够成熟，因为它才刚刚开始，但它有望作为估算波荷载和最终方法。

上述方法的优缺点见表1。

考虑到准确性、稳定性、计算时间以及和长期预测法的兼容性，本文采取了基于频域和三维势流理论的估算方法。

非线性特性及波浪限度、运动振幅则是利用了大浪条件下油轮测试得到的结果。

表1各种估算船体梁响应数值法的优缺点A：线性带状法；B：频域三维势流理论；C：时域三维势流理论；D：CFD。

◎：很好；○：好/可考虑；△：不好；×：差/不考虑。

2.2 兰金源法表2集装箱船模型的主要情况图1 三个断面处的力用力传感器测量水池试验的波浪条件见表3。

该试验在常规波条件下进行，分别以三种不同的入射波高，10种波长，7七种入射波角度（以301为间隔从1801（顶头浪）到01（尾随浪））以及2种不同的船速。

短波范围内不能进行15米波高的水池试验，入射波会在这个高度破裂。

2.4 数值结果比较图2〔a〕—〔c〕表示了首斜浪（120°）中三个断面（站线为2.5，5.0和7.5处）响应函数振幅的比较，或所谓的扭力矩响应振幅算子的比较。

横坐标显示波长λ随船舶高度L(λ/L)而变化的情况,纵坐标则显示单位波幅的扭力矩振幅。

试验结果利用傅里叶函数进行分析，因此，水池试验值表明元件振幅周期与遭遇波周期相同。

图中，“Exp.(3.5m)”, “Exp.(9.0m)”, “Exp.(15.0m)”, “STRIP” and “Rankine”分别代表3种入射波高度条件下的试验结果，带状法及兰金源法分析结果。

计算力传感器纵向位置或略低于静止水位的扭力矩。

表3水池试验的波浪条件兰金源法得到的数值结果与3.5米波高时的试验结果有很好的一致性，尤其在短波范围内，如图2所示。

而带状法得到的数值结果则与试验结果有较大出入。

图2 站线2.5（a），5.0（b），7.5（c）处试验结果与数值分析法结果的比较。

数据还显示在首斜浪（120°）的更短波长范围内结果最大。

此外，船尾处（站线2.5）也比其他地方（站线5.0，7.5）的结果大。

2.5 波中扭力矩的非线性特性波浪高度使扭力矩具有非线性特征，继而造成如图2(a)–(c)中“Exp.(3.5m)”,“Exp.(9.0m)”and“Exp.(15.0m)”处试验结果的不同。

随着波浪高度的增加，船头尾处每单位入射波的扭力矩增大，显示出明显的非线性特征。

而船尾和船中间不同波高的三个值和扭力矩几乎相同，非线性特征微弱。

试验中利用扭力矩 RAOs 得到与船中间部位有关的剪力中心处短期和长期扭力矩预测值。

短期和长期预测分别用到了社科理事会1964波光谱（定向分配：余弦2）和国际船级社协会波数据（北大西洋，全年[29]）。

采用福田康夫提出的方法作了长期预测。

然后，比较大波浪与线性小波浪中的长期扭力矩预测值，定量确定波荷载的定义为在代表线性因素的大、小波浪非线性相关系数。

非线性相关系数Cnonlinear条件下扭力矩长期预测值的比值。

( 1)其中[X max]Hw=9.0m and [X max]Hw=3.5m分别代表概率水平为10-8时波高9.0m和3.5m 条件下利用RAOs得到的扭力矩长期预测值。

因此，假定相关响应的试验结果在3.5m波高条件下呈线性关系，又因为短波范围内高15米的波浪会破碎，扭力矩会达到显著值，同时假定波高9m时非线性关系使扭力矩的增加量达到最大值。

表4显示了三个不同地点处(站线为2.5、5.0 和7.5)与扭力矩有关的非线性相关系数值。

由表可发现船中间和船尾（站线为2.5，5.0）处非线性特征不明显，而船头（站线为7.5）处扭力矩的非线性相关系数则非常大。

表4与波高有关的扭力矩非线性相关系数试验结果3.结构响应估计3.1 概述材料力学表明，在大断面梁上扭转响应和横向响应会发生耦合。

为了避免翘曲应力和横向弯曲应力发生耦合，要采用的标准程序是评估与船中间部位有关的扭力矩剪切中心，并定义翘曲应力为扭力矩下的船体梁应力。

但是，一个真正的而变化，故应力分量不能被完全分开。

另一方面，翘曲应力σWT也可定义为扣除横向弯曲应力后横向剪切应力和扭力矩下的船体梁应力，结构上可表达为σWT ＝σHS＋σTM －σWH，其中σHS ，σTM 和σWH分别表示横向剪切应力、扭力矩和横向弯曲应力下的船体梁应力。

这一定义中，不需要假定剪切中心的高度。

通过研究计算，由第一个定义得到的翘曲应力的响应函数与第二个定义得到的结果几乎相同。

船舶翻译文献

船舶工程外文翻译----船舶结构安全性和可靠性

船舶设计外文文献翻译

船舶制造中英文对照外文翻译文献

船舶设计外文文献翻译

船舶稳性文献翻译-魏豪

船舶设计论文中英文外文翻译文献

介绍船舶英语作文带翻译

(完整版)船舶专业英语(课文+翻译)

船舶专业英语(课文+翻译)

有关船舶的文章 英文版范文

船舶设计论文中英文外文翻译文献

船舶论文 外文翻译

船舶论文-外文翻译

船舶与海洋工程论文中英文资料外文翻译文献

船舶专业外文文献之欧阳物创编

船舶翻译资料

介绍船体英语作文带翻译

船舶专业外文文献之欧阳地创编

船舶专业外文文献之欧阳与创编

有关船舶的文章英文版范文

船舶论文外文翻译