A dynamic lot-sizing model with demand time windows

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最近鲁棒优化进展Recent Advances in Robust Optimization and Robustness An Overview

Recent Advances in Robust Optimization and Robustness:An OverviewVirginie Gabrel∗and C´e cile Murat†and Aur´e lie Thiele‡July2012AbstractThis paper provides an overview of developments in robust optimization and robustness published in the aca-demic literature over the pastﬁve years.1IntroductionThis review focuses on papers identiﬁed by Web of Science as having been published since2007(included),be-longing to the area of Operations Research and Management Science,and having‘robust’and‘optimization’in their title.There were exactly100such papers as of June20,2012.We have completed this list by considering 726works indexed by Web of Science that had either robustness(for80of them)or robust(for646)in their title and belonged to the Operations Research and Management Science topic area.We also identiﬁed34PhD disserta-tions dated from the lastﬁve years with‘robust’in their title and belonging to the areas of operations research or management.Among those we have chosen to focus on the works with a primary focus on management science rather than system design or optimal control,which are broadﬁelds that would deserve a review paper of their own, and papers that could be of interest to a large segment of the robust optimization research community.We feel it is important to include PhD dissertations to identify these recent graduates as the new generation trained in robust optimization and robustness analysis,whether they have remained in academia or joined industry.We have also added a few not-yet-published preprints to capture ongoing research efforts.While many additional works would have deserved inclusion,we feel that the works selected give an informative and comprehensive view of the state of robustness and robust optimization to date in the context of operations research and management science.∗Universit´e Paris-Dauphine,LAMSADE,Place du Mar´e chal de Lattre de Tassigny,F-75775Paris Cedex16,France gabrel@lamsade.dauphine.fr Corresponding author†Universit´e Paris-Dauphine,LAMSADE,Place du Mar´e chal de Lattre de Tassigny,F-75775Paris Cedex16,France mu-rat@lamsade.dauphine.fr‡Lehigh University,Industrial and Systems Engineering Department,200W Packer Ave Bethlehem PA18015,USA aure-lie.thiele@2Theory of Robust Optimization and Robustness2.1Deﬁnitions and BasicsThe term“robust optimization”has come to encompass several approaches to protecting the decision-maker against parameter ambiguity and stochastic uncertainty.At a high level,the manager must determine what it means for him to have a robust solution:is it a solution whose feasibility must be guaranteed for any realization of the uncertain parameters?or whose objective value must be guaranteed?or whose distance to optimality must be guaranteed? The main paradigm relies on worst-case analysis:a solution is evaluated using the realization of the uncertainty that is most unfavorable.The way to compute the worst case is also open to debate:should it use aﬁnite number of scenarios,such as historical data,or continuous,convex uncertainty sets,such as polyhedra or ellipsoids?The answers to these questions will determine the formulation and the type of the robust counterpart.Issues of over-conservatism are paramount in robust optimization,where the uncertain parameter set over which the worst case is computed should be chosen to achieve a trade-off between system performance and protection against uncertainty,i.e.,neither too small nor too large.2.2Static Robust OptimizationIn this framework,the manager must take a decision in the presence of uncertainty and no recourse action will be possible once uncertainty has been realized.It is then necessary to distinguish between two types of uncertainty: uncertainty on the feasibility of the solution and uncertainty on its objective value.Indeed,the decision maker generally has different attitudes with respect to infeasibility and sub-optimality,which justiﬁes analyzing these two settings separately.2.2.1Uncertainty on feasibilityWhen uncertainty affects the feasibility of a solution,robust optimization seeks to obtain a solution that will be feasible for any realization taken by the unknown coefﬁcients;however,complete protection from adverse realiza-tions often comes at the expense of a severe deterioration in the objective.This extreme approach can be justiﬁed in some engineering applications of robustness,such as robust control theory,but is less advisable in operations research,where adverse events such as low customer demand do not produce the high-proﬁle repercussions that engineering failures–such as a doomed satellite launch or a destroyed unmanned robot–can have.To make the robust methodology appealing to business practitioners,robust optimization thus focuses on obtaining a solution that will be feasible for any realization taken by the unknown coefﬁcients within a smaller,“realistic”set,called the uncertainty set,which is centered around the nominal values of the uncertain parameters.The goal becomes to optimize the objective,over the set of solutions that are feasible for all coefﬁcient values in the uncertainty set.The speciﬁc choice of the set plays an important role in ensuring computational tractability of the robust problem and limiting deterioration of the objective at optimality,and must be thought through carefully by the decision maker.A large branch of robust optimization focuses on worst-case optimization over a convex uncertainty set.The reader is referred to Bertsimas et al.(2011a)and Ben-Tal and Nemirovski(2008)for comprehensive surveys of robust optimization and to Ben-Tal et al.(2009)for a book treatment of the topic.2.2.2Uncertainty on objective valueWhen uncertainty affects the optimality of a solution,robust optimization seeks to obtain a solution that performs well for any realization taken by the unknown coefﬁcients.While a common criterion is to optimize the worst-case objective,some studies have investigated other robustness measures.Roy(2010)proposes a new robustness criterion that holds great appeal for the manager due to its simplicity of use and practical relevance.This framework,called bw-robustness,allows the decision-maker to identify a solution which guarantees an objective value,in a maximization problem,of at least w in all scenarios,and maximizes the probability of reaching a target value of b(b>w).Gabrel et al.(2011)extend this criterion from aﬁnite set of scenarios to the case of an uncertainty set modeled using intervals.Kalai et al.(2012)suggest another criterion called lexicographicα-robustness,also deﬁned over aﬁnite set of scenarios for the uncertain parameters,which mitigates the primary role of the worst-case scenario in deﬁning the solution.Thiele(2010)discusses over-conservatism in robust linear optimization with cost uncertainty.Gancarova and Todd(2012)studies the loss in objective value when an inaccurate objective is optimized instead of the true one, and shows that on average this loss is very small,for an arbitrary compact feasible region.In combinatorial optimization,Morrison(2010)develops a framework of robustness based on persistence(of decisions)using the Dempster-Shafer theory as an evidence of robustness and applies it to portfolio tracking and sensor placement.2.2.3DualitySince duality has been shown to play a key role in the tractability of robust optimization(see for instance Bertsimas et al.(2011a)),it is natural to ask how duality and robust optimization are connected.Beck and Ben-Tal(2009) shows that primal worst is equal to dual best.The relationship between robustness and duality is also explored in Gabrel and Murat(2010)when the right-hand sides of the constraints are uncertain and the uncertainty sets are represented using intervals,with a focus on establishing the relationships between linear programs with uncertain right hand sides and linear programs with uncertain objective coefﬁcients using duality theory.This avenue of research is further explored in Gabrel et al.(2010)and Remli(2011).2.3Multi-Stage Decision-MakingMost early work on robust optimization focused on static decision-making:the manager decided at once of the values taken by all decision variables and,if the problem allowed for multiple decision stages as uncertainty was realized,the stages were incorporated by re-solving the multi-stage problem as time went by and implementing only the decisions related to the current stage.As theﬁeld of static robust optimization matured,incorporating–ina tractable manner–the information revealed over time directly into the modeling framework became a major area of research.2.3.1Optimal and Approximate PoliciesA work going in that direction is Bertsimas et al.(2010a),which establishes the optimality of policies afﬁne in the uncertainty for one-dimensional robust optimization problems with convex state costs and linear control costs.Chen et al.(2007)also suggests a tractable approximation for a class of multistage chance-constrained linear program-ming problems,which converts the original formulation into a second-order cone programming problem.Chen and Zhang(2009)propose an extension of the Afﬁnely Adjustable Robust Counterpart framework described in Ben-Tal et al.(2009)and argue that its potential is well beyond what has been in the literature so far.2.3.2Two stagesBecause of the difﬁculty in incorporating multiple stages in robust optimization,many theoretical works have focused on two stages.Regarding two-stage problems,Thiele et al.(2009)presents a cutting-plane method based on Kelley’s algorithm for solving convex adjustable robust optimization problems,while Terry(2009)provides in addition preliminary results on the conditioning of a robust linear program and of an equivalent second-order cone program.Assavapokee et al.(2008a)and Assavapokee et al.(2008b)develop tractable algorithms in the case of robust two-stage problems where the worst-case regret is minimized,in the case of interval-based uncertainty and scenario-based uncertainty,respectively,while Minoux(2011)provides complexity results for the two-stage robust linear problem with right-hand-side uncertainty.2.4Connection with Stochastic OptimizationAn early stream in robust optimization modeled stochastic variables as uncertain parameters belonging to a known uncertainty set,to which robust optimization techniques were then applied.An advantage of this method was to yield approaches to decision-making under uncertainty that were of a level of complexity similar to that of their deterministic counterparts,and did not suffer from the curse of dimensionality that afﬂicts stochastic and dynamic programming.Researchers are now making renewed efforts to connect the robust optimization and stochastic opti-mization paradigms,for instance quantifying the performance of the robust optimization solution in the stochastic world.The topic of robust optimization in the context of uncertain probability distributions,i.e.,in the stochastic framework itself,is also being revisited.2.4.1Bridging the Robust and Stochastic WorldsBertsimas and Goyal(2010)investigates the performance of static robust solutions in two-stage stochastic and adaptive optimization problems.The authors show that static robust solutions are good-quality solutions to the adaptive problem under a broad set of assumptions.They provide bounds on the ratio of the cost of the optimal static robust solution to the optimal expected cost in the stochastic problem,called the stochasticity gap,and onthe ratio of the cost of the optimal static robust solution to the optimal cost in the two-stage adaptable problem, called the adaptability gap.Chen et al.(2007),mentioned earlier,also provides a robust optimization perspective to stochastic programming.Bertsimas et al.(2011a)investigates the role of geometric properties of uncertainty sets, such as symmetry,in the power ofﬁnite adaptability in multistage stochastic and adaptive optimization.Duzgun(2012)bridges descriptions of uncertainty based on stochastic and robust optimization by considering multiple ranges for each uncertain parameter and setting the maximum number of parameters that can fall within each range.The corresponding optimization problem can be reformulated in a tractable manner using the total unimodularity of the feasible set and allows for aﬁner description of uncertainty while preserving tractability.It also studies the formulations that arise in robust binary optimization with uncertain objective coefﬁcients using the Bernstein approximation to chance constraints described in Ben-Tal et al.(2009),and shows that the robust optimization problems are deterministic problems for modiﬁed values of the coefﬁcients.While many results bridging the robust and stochastic worlds focus on giving probabilistic guarantees for the solutions generated by the robust optimization models,Manuja(2008)proposes a formulation for robust linear programming problems that allows the decision-maker to control both the probability and the expected value of constraint violation.Bandi and Bertsimas(2012)propose a new approach to analyze stochastic systems based on robust optimiza-tion.The key idea is to replace the Kolmogorov axioms and the concept of random variables as primitives of probability theory,with uncertainty sets that are derived from some of the asymptotic implications of probability theory like the central limit theorem.The authors show that the performance analysis questions become highly structured optimization problems for which there exist efﬁcient algorithms that are capable of solving problems in high dimensions.They also demonstrate that the proposed approach achieves computationally tractable methods for(a)analyzing queueing networks,(b)designing multi-item,multi-bidder auctions with budget constraints,and (c)pricing multi-dimensional options.2.4.2Distributionally Robust OptimizationBen-Tal et al.(2010)considers the optimization of a worst-case expected-value criterion,where the worst case is computed over all probability distributions within a set.The contribution of the work is to deﬁne a notion of robustness that allows for different guarantees for different subsets of probability measures.The concept of distributional robustness is also explored in Goh and Sim(2010),with an emphasis on linear and piecewise-linear decision rules to reformulate the original problem in aﬂexible manner using expected-value terms.Xu et al.(2012) also investigates probabilistic interpretations of robust optimization.A related area of study is worst-case optimization with partial information on the moments of distributions.In particular,Popescu(2007)analyzes robust solutions to a certain class of stochastic optimization problems,using mean-covariance information about the distributions underlying the uncertain parameters.The author connects the problem for a broad class of objective functions to a univariate mean-variance robust objective and,subsequently, to a(deterministic)parametric quadratic programming problem.The reader is referred to Doan(2010)for a moment-based uncertainty model for stochastic optimization prob-lems,which addresses the ambiguity of probability distributions of random parameters with a minimax decision rule,and a comparison with data-driven approaches.Distributionally robust optimization in the context of data-driven problems is the focus of Delage(2009),which uses observed data to deﬁne a”well structured”set of dis-tributions that is guaranteed with high probability to contain the distribution from which the samples were drawn. Zymler et al.(2012a)develop tractable semideﬁnite programming(SDP)based approximations for distributionally robust individual and joint chance constraints,assuming that only theﬁrst-and second-order moments as well as the support of the uncertain parameters are given.Becker(2011)studies the distributionally robust optimization problem with known mean,covariance and support and develops a decomposition method for this family of prob-lems which recursively derives sub-policies along projected dimensions of uncertainty while providing a sequence of bounds on the value of the derived policy.Robust linear optimization using distributional information is further studied in Kang(2008).Further,Delage and Ye(2010)investigates distributional robustness with moment uncertainty.Speciﬁcally,uncertainty affects the problem both in terms of the distribution and of its moments.The authors show that the resulting problems can be solved efﬁciently and prove that the solutions exhibit,with high probability,best worst-case performance over a set of distributions.Bertsimas et al.(2010)proposes a semideﬁnite optimization model to address minimax two-stage stochastic linear problems with risk aversion,when the distribution of the second-stage random variables belongs to a set of multivariate distributions with knownﬁrst and second moments.The minimax solutions provide a natural distribu-tion to stress-test stochastic optimization problems under distributional ambiguity.Cromvik and Patriksson(2010a) show that,under certain assumptions,global optima and stationary solutions of stochastic mathematical programs with equilibrium constraints are robust with respect to changes in the underlying probability distribution.Works such as Zhu and Fukushima(2009)and Zymler(2010)also study distributional robustness in the context of speciﬁc applications,such as portfolio management.2.5Connection with Risk TheoryBertsimas and Brown(2009)describe how to connect uncertainty sets in robust linear optimization to coherent risk measures,an example of which is Conditional Value-at-Risk.In particular,the authors show the link between polyhedral uncertainty sets of a special structure and a subclass of coherent risk measures called distortion risk measures.Independently,Chen et al.(2007)present an approach for constructing uncertainty sets for robust opti-mization using new deviation measures that capture the asymmetry of the distributions.These deviation measures lead to improved approximations of chance constraints.Dentcheva and Ruszczynski(2010)proposes the concept of robust stochastic dominance and shows its applica-tion to risk-averse optimization.They consider stochastic optimization problems where risk-aversion is expressed by a robust stochastic dominance constraint and develop necessary and sufﬁcient conditions of optimality for such optimization problems in the convex case.In the nonconvex case,they derive necessary conditions of optimality under additional smoothness assumptions of some mappings involved in the problem.2.6Nonlinear OptimizationRobust nonlinear optimization remains much less widely studied to date than its linear counterpart.Bertsimas et al.(2010c)presents a robust optimization approach for unconstrained non-convex problems and problems based on simulations.Such problems arise for instance in the partial differential equations literature and in engineering applications such as nanophotonic design.An appealing feature of the approach is that it does not assume any speciﬁc structure for the problem.The case of robust nonlinear optimization with constraints is investigated in Bertsimas et al.(2010b)with an application to radiation therapy for cancer treatment.Bertsimas and Nohadani (2010)further explore robust nonconvex optimization in contexts where solutions are not known explicitly,e.g., have to be found using simulation.They present a robust simulated annealing algorithm that improves performance and robustness of the solution.Further,Boni et al.(2008)analyzes problems with uncertain conic quadratic constraints,formulating an approx-imate robust counterpart,and Zhang(2007)provide formulations to nonlinear programming problems that are valid in the neighborhood of the nominal parameters and robust to theﬁrst order.Hsiung et al.(2008)present tractable approximations to robust geometric programming,by using piecewise-linear convex approximations of each non-linear constraint.Geometric programming is also investigated in Shen et al.(2008),where the robustness is injected at the level of the algorithm and seeks to avoid obtaining infeasible solutions because of the approximations used in the traditional approach.Interval uncertainty-based robust optimization for convex and non-convex quadratic programs are considered in Li et al.(2011).Takeda et al.(2010)studies robustness for uncertain convex quadratic programming problems with ellipsoidal uncertainties and proposes a relaxation technique based on random sampling for robust deviation optimization sserre(2011)considers minimax and robust models of polynomial optimization.A special case of nonlinear problems that are linear in the decision variables but convex in the uncertainty when the worst-case objective is to be maximized is investigated in Kawas and Thiele(2011a).In that setting,exact and tractable robust counterparts can be derived.A special class of nonconvex robust optimization is examined in Kawas and Thiele(2011b).Robust nonconvex optimization is examined in detail in Teo(2007),which presents a method that is applicable to arbitrary objective functions by iteratively moving along descent directions and terminates at a robust local minimum.3Applications of Robust OptimizationWe describe below examples to which robust optimization has been applied.While an appealing feature of robust optimization is that it leads to models that can be solved using off-the-shelf software,it is worth pointing the existence of algebraic modeling tools that facilitate the formulation and subsequent analysis of robust optimization problems on the computer(Goh and Sim,2011).3.1Production,Inventory and Logistics3.1.1Classical logistics problemsThe capacitated vehicle routing problem with demand uncertainty is studied in Sungur et al.(2008),with a more extensive treatment in Sungur(2007),and the robust traveling salesman problem with interval data in Montemanni et al.(2007).Remli and Rekik(2012)considers the problem of combinatorial auctions in transportation services when shipment volumes are uncertain and proposes a two-stage robust formulation solved using a constraint gener-ation algorithm.Zhang(2011)investigates two-stage minimax regret robust uncapacitated lot-sizing problems with demand uncertainty,in particular showing that it is polynomially solvable under the interval uncertain demand set.3.1.2SchedulingGoren and Sabuncuoglu(2008)analyzes robustness and stability measures for scheduling in a single-machine environment subject to machine breakdowns and embeds them in a tabu-search-based scheduling algorithm.Mittal (2011)investigates efﬁcient algorithms that give optimal or near-optimal solutions for problems with non-linear objective functions,with a focus on robust scheduling and service operations.Examples considered include parallel machine scheduling problems with the makespan objective,appointment scheduling and assortment optimization problems with logit choice models.Hazir et al.(2010)considers robust scheduling and robustness measures for the discrete time/cost trade-off problem.3.1.3Facility locationAn important question in logistics is not only how to operate a system most efﬁciently but also how to design it. Baron et al.(2011)applies robust optimization to the problem of locating facilities in a network facing uncertain demand over multiple periods.They consider a multi-periodﬁxed-charge network location problem for which they ﬁnd the number of facilities,their location and capacities,the production in each period,and allocation of demand to facilities.The authors show that different models of uncertainty lead to very different solution network topologies, with the model with box uncertainty set opening fewer,larger facilities.?investigate a robust version of the location transportation problem with an uncertain demand using a2-stage formulation.The resulting robust formulation is a convex(nonlinear)program,and the authors apply a cutting plane algorithm to solve the problem exactly.Atamt¨u rk and Zhang(2007)study the networkﬂow and design problem under uncertainty from a complexity standpoint,with applications to lot-sizing and location-transportation problems,while Bardossy(2011)presents a dual-based local search approach for deterministic,stochastic,and robust variants of the connected facility location problem.The robust capacity expansion problem of networkﬂows is investigated in Ordonez and Zhao(2007),which provides tractable reformulations under a broad set of assumptions.Mudchanatongsuk et al.(2008)analyze the network design problem under transportation cost and demand uncertainty.They present a tractable approximation when each commodity only has a single origin and destination,and an efﬁcient column generation for networks with path constraints.Atamt¨u rk and Zhang(2007)provides complexity results for the two-stage networkﬂow anddesign plexity results for the robust networkﬂow and network design problem are also provided in Minoux(2009)and Minoux(2010).The problem of designing an uncapacitated network in the presence of link failures and a competing mode is investigated in Laporte et al.(2010)in a railway application using a game theoretic perspective.Torres Soto(2009)also takes a comprehensive view of the facility location problem by determining not only the optimal location but also the optimal time for establishing capacitated facilities when demand and cost parameters are time varying.The models are solved using Benders’decomposition or heuristics such as local search and simulated annealing.In addition,the robust networkﬂow problem is also analyzed in Boyko(2010),which proposes a stochastic formulation of minimum costﬂow problem aimed atﬁnding network design andﬂow assignments subject to uncertain factors,such as network component disruptions/failures when the risk measure is Conditional Value at Risk.Nagurney and Qiang(2009)suggests a relative total cost index for the evaluation of transportation network robustness in the presence of degradable links and alternative travel behavior.Further,the problem of locating a competitive facility in the plane is studied in Blanquero et al.(2011)with a robustness criterion.Supply chain design problems are also studied in Pan and Nagi(2010)and Poojari et al.(2008).3.1.4Inventory managementThe topic of robust multi-stage inventory management has been investigated in detail in Bienstock and Ozbay (2008)through the computation of robust basestock levels and Ben-Tal et al.(2009)through an extension of the Afﬁnely Adjustable Robust Counterpart framework to control inventories under demand uncertainty.See and Sim (2010)studies a multi-period inventory control problem under ambiguous demand for which only mean,support and some measures of deviations are known,using a factor-based model.The parameters of the replenishment policies are obtained using a second-order conic programming problem.Song(2010)considers stochastic inventory control in robust supply chain systems.The work proposes an inte-grated approach that combines in a single step dataﬁtting and inventory optimization–using histograms directly as the inputs for the optimization model–for the single-item multi-period periodic-review stochastic lot-sizing problem.Operation and planning issues for dynamic supply chain and transportation networks in uncertain envi-ronments are considered in Chung(2010),with examples drawn from emergency logistics planning,network design and congestion pricing problems.3.1.5Industry-speciﬁc applicationsAng et al.(2012)proposes a robust storage assignment approach in unit-load warehouses facing variable supply and uncertain demand in a multi-period setting.The authors assume a factor-based demand model and minimize the worst-case expected total travel in the warehouse with distributional ambiguity of demand.A related problem is considered in Werners and Wuelﬁng(2010),which optimizes internal transports at a parcel sorting center.Galli(2011)describes the models and algorithms that arise from implementing recoverable robust optimization to train platforming and rolling stock planning,where the concept of recoverable robustness has been deﬁned in。

外资公司日常管理英语常用词汇

外资公司日常管理英语常用词汇一、组织机构及职位总经理办公室General manager’s office企管部 Enterprise management department (EM)行政部 Administration department (AD)销售部 Sales department (SD)财务部 Financial department (FD)技术部 Technology department (TD)物控部 Production material control department (PMC)生产部 Production department(PD)模具部 Mold manufacturing department, Tooling manufacturing department 品管部 Quality Assurance department (QA)冲压车间 Stamp workshop, press workshop注塑车间 injection workshop装配车间 Assembly workshop模具装配车间 Mold and die Assembly workshop金属加工车间 Metal machine workshop电脉冲车间 Electric discharge process workshop线切割车间 Wire cutting process workshop工磨车间 Grinding workshop总经理 General manager (GM)副总经理 Vice-general manager经理 Manager董事长 President副董事长 Vice-presidentXX部门经理 Manager of XX department 主任、主管 supervisor拉长 Line leader组长 Foreman, forelady秘书 secretary文员 clerk操作员 operator助理 assistant职员 staff二、产品连接器 connector端子 terminal条型连接器 bar connector阴连接器 Housing阳连接器 wafer线束 wire harness间距 space额定电压 rated voltage额定电流 rated current接触电阻 contact resistance绝缘电阻 insulation resistance超声波焊接 ultrasonic welding 耐压 withstand voltage 针 pin物料编号 part number导线 wire基体金属 Base metal电缆夹 cable clamp倒角 chamfer接触面积 contact area接触件安装孔 contact cavity接触长度 contact length接触件电镀层 contact plating接触压力 contact pressure 接触件中心距 contact space接触簧片 contact spring接触孔 socket contact法兰、凸缘 Flange界面间隙 interfacial gap键 Key键槽 keyway过渡段 ramp屏蔽套 shielding定位基准 Datum reference扁平电缆 flat cable ，Ribbon cable柔性印刷电线 Flexible printed wiring多层印制电路 Multilayer printed circuit焊盘 pad图形 pattern间距 pitch负极 Negative pole正极 positive pole回流 Reflow原理图 Schematic diagram单面板 single sided board双面板 Two-sided board，Double-sided board 表面安装 surface Mounting翘曲 warp， bow波峰焊 wave soldering编织层 braid同轴电缆 coaxial cable电介质 dielectric电缆中导线的头数ends外部干扰 external interference填充物 filler护套 jacket比重 specific gravity电阻的温度系数Temperature coefficient of resistance三、模具塑料模具 mould of plastics注塑模具 injection mould冲压模具 die模架 mould base定模座板 Top clamping plate /Top plate /Fixed clamp plate 水口推板 stripper plateA板 A plateB板 B plate支承板 support plate方铁 spacer plate底针板 ejector plate面针板 ejector retainer plate回针 Return pin导柱 Guide pin有托导套 Shoulder Guide bush直导套 Straight Guide bush动模座板Bottom clamp plateMoving clamp plate基准线datum line基准面datum plan型芯固定板core-retainer plate 凸模固定板punch-retainer plate 顶针ejector pin单腔模具single cavity mould多腔模具multi-cavity mould多浇口multi-gating浇口gate缺料starving排气breathing光泽gloss合模力mould clamping force锁模力mould locking force挤出extrusion开裂crack循环时间cycle time老化aging螺杆screw麻点pit嵌件insert活动镶件movable insert起垩chalking浇注系统feed system主流道 sprue分流道runner浇口gate直浇口direct gate , sprue gate轮辐浇口spoke gate , spider gate点浇口pin-point gate测浇口edge gate潜伏浇口submarine gate , tunnel gate 料穴cold-slug well浇口套sprue bush流道板runner plate排飞槽vent分型线（面）parting line定模stationary mould，Fixed mould动模movable mould, movable half上模upper mould， upper half下模lower mould， lower half型腔cavity凹模cavity plate，cavity block拼块split 定位销dowel定位销孔dowel hole型芯core斜销angle pin, finger cam滑块slide滑块导板slide guide strip楔紧块heel block, wedge lock拉料杆sprue puller定位环locating ring冷却通cooling channel脱模斜度draft滑动型芯slide core螺纹型芯threaded core热流道模具hot-runner mould绝热流道模insulated runner mould熔合纹weld line （flow line）三板式模具three plate mould脱模ejection脱模剂release agent注射能力shot capacity注射速率injection rate注射压力injection pressure差色剂colorant保压时间holdup time闭模时间closing time定型装置sizing system阴模female mould，cavity block阳模male mould电加工设备Electron Discharge Machining数控加工中心CNC machine center万能铁床Universal milling machine卧式刨床Horizontal planer车床Engine lathe平面磨床Surface grinding machine去磁机Demagnetization machine 换模腔模具 interchangeable cavity mould万能摇臂钻床Universal radial movable driller立式钻床Vertical driller超声波清洗机Ultrasonic clearing machine四、品管SPC statistic process control品管保证Quality Assurance品管控制Quality control来料检验IQC Incoming quality control巡检IPQC In-process quality control校对calibration动态试验dynamic test环境试验Environmental test非破坏性试验non-destructive test光泽gloss击穿电压(dielectric) breakdown voltage拉伸强度tensile strength冷热骤变试验thermal shock test环境试验炉Environmental chamber盐雾实验salt spray test绝缘电阻测试验仪Insulating resistance meter内应力internal stress疲劳fatigue蠕变creep试样specimen撕裂强度tear strength缩痕shrink mark, sink mark耐久性durability抽样sampling样品数量sample sizeAQL Acceptable Quality level批量lot size抽样计划sampling planOC曲线operation curve试验顺序sequence of tests 环境温度ambient temperature 可焊性solderability阻燃性flame resistance五、生产注塑机injection machine冲床Punch machine混料机blender mixer尼龙nylon黄铜 brass青铜 bronze紫(纯)铜 copper料斗hopper麻点pit配料compounding涂层coating飞边flash预热preheating再生料reworked material再生塑料reworked plastics工艺设计process design紧急停止emergency stop延时time delay六．物控保质期shelf lifeABC分类法ABC Classification反常需求Abnormal Demand措施信息Action Message活动报告标志Action-report-flag基于活动的成本核算Activity-based Costing (ABC)实际能力Actual Capacity实际成本Actual Costs调整现有库存量Adjust-on-hand已分配量Allocation Alternative Routine 装配订单Assembly Order装配零件表Assembly Parts List装配Assembly计划自动重排Automatic Rescheduling可达到库存Available Inventory可用材料Available Material达到库存Available Stock可利用工时Available Work可签约量Available-to-promise平均库存Average Inventory欠交订单Back Order倒序计划Back Scheduling倒冲法Back flush未完成订单Backlog现有库存余额Balance-on-hand Inventory批号Batch Number批量生产Batch Production标杆瞄准Benchmarking工时清单Bill of Labor提货单Bill of Lading物料清单Bill of Material分库Branch Warehouse经营规划Business Plan采购员Buyer能力管理Capacity Management能力需求计划Capacity Requirements Planning (CRP)保管费率Carrying Cost Rate保管费Carrying Cost单元式制造Cellular Manufacturing修改批量日期Change Lot Date修改工序Change Route修改产品结构Change Structure检查点Check Point闭环物料需求计划Closed Loop MRP通用工序标识Common Route ID计算机集成制造Computer-integrated Manufacturing (CIM)配置代码Configuration Code约束管理/约束理论Constraints Management/Theory of Constraints (TOC) 依成本的材料清单Costed Bill of Material急需零件Critical Part累计提前期Cumulative Lead Time现有运转时间Current Run Hour现有运转数量Current Run Quantity周期盘点Cycle Counting调整日期Date Adjust有效日期Date Available修改日期Date Changed结束日期Date Closed截止日期Date Due生产日期Date in Produced库存调整日期Date Inventory Adjust作废日期D ate Obsolete收到日期Date Received交付日期Date Released需求日期Date Required发货日期Date to Pull空负荷Dead Load需求管理Demand Management需求Demand实际能力Demonstrated Capacity非独立需求Dependent Demand直接增减库存处理法Direct-deduct Inventory Transaction Processing 发料单Disbursement List派工单Dispatch List分销资源计划Distribution Resource Planning (DRP)鼓－缓冲－绳子Drum-buffer-rope经济订货批量Economic Order Quantity (EOQ)工程变更生效日期Engineering Change Effect Date工程变更生效单Engineering Change Effect Work Order工程停止日期Engineering Stop Date例外控制Exception Control呆滞材料分析Excess Material Analysis急送代码Expedite Code加工订单Fabrication Order补足欠交Fill Backorder总装提前期Final Assembly Lead Time确认的计划订单Firm Planned Order固定订货批量F ixed Order Quantity集中预测Focus Forecasting完全跟踪Full Pegging通用生产管理原则Generally Accepted Manufacturing Practices 毛需求Gross Requirements在制品库存In Process Inventory独立需求Independent Demand投入/产出控制Input/ Output Control检验标识Inspection ID厂际需求Interplant Demand库存周转率Inventory Carry Rate仓库库位类型Inventory Location Type 库存周转次数Inventory Turnover发送订单Issue Order项目记录Item Record物料项目Item加工车间Job Shop准时制生产Just-in-time (JIT)看板Kanban人工工时Labor Hour最后运输日期Last Shipment Date提前期Lead Time层Level负荷量Load Leveling负荷报告Load Report负荷Load仓位代码Location Code仓位备注Location Remarks仓位状况Location Status按需订货Lot for Lot批量标识Lot ID批量编号Lot Number批量Lot Size低位码Low Level Code机器能力Machine Capacity机器加载Machine Loading外购或自制决策Make or Buy Decision面向订单生产的产品Make-to-order Product面向库存生产的产品Make-to-stock Product制造周期时间Manufacturing Cycle Time制造资源计划Manufacturing Resource Planning (MRP II) 主生产计划Master Production Schedule (MPS)物料成本Material Cost物料发送和接收Material Issues and Receipts物料需求计划Material Requirements Planning (MRP)登陆标志MPS Book Flag MPS多重仓位Multiple Location净改变式MRP N et Change MRP净需求Net Requirements新仓位New Location新组件New Parent新仓库New Warehouse不活动报告No Action Report现有库存量On-hand Balance未结订单Open Order订单输入Order Entry订货点Order Point订货方针Order Policy订货承诺Order Promising订货备注Order Remarks双亲Parent零件清单Part Bills零件批次Part Lot零件编号Part Number零件Part反查Pegging领料单Picking List领料/提货Picking计划订单Planned Order后减库存处理法Post-deduct Inventory Transaction Processing 前减库存处理法Pre-deduct Inventory Transaction Processing 发票价格Price Invoice采购订单价格Price Purchase Order优先计划Priority Planning产品控制Product Control产品线Production Line生产规划编制Production Planning产品率Production Rate产品结构树Production Tree预计可用库存Projected Available Balance采购订单跟踪Purchase Order Tracking已分配量Quantity Allocation仓位数量Quantity At Location欠交数量Quantity Backorder完成数量Quantity Completion需求量Quantity Demand毛需求量Quantity Gross进货数量Quantity In排队时间Queue Time队列Queue重生成式MRP Regenerated MRP重排假设Rescheduling Assumption资源需求计划Resource Requirements Planning 粗能力计划Rough-cut Capacity Planning工艺路线Routing安全库存量Safety Stock保险期Safety Time预计入库量Scheduled Receipt残料率Scrap Factor发送零件Send Part维修件Service Parts发货地址Ship Address发货单联系人Ship Contact发货零件Ship Date发货单Ship Order工厂日历Shop Calendar车间作业管理Shop Floor Control损耗系数Shrinkage Factor标准产品成本Standard Product Cost标准机器设置工时Standard Set Up Hour 标准单位运转工时Standard Unit Run Hour 标准工资率Standard Wage Rate状况代码Status Code库存控制Stores Control建议工作单Suggested Work Order约束理论Theory of Constraints (TOC)时间段Time Bucket时界Time Fence单位成本Unit Cost采购计划员Vendor Scheduler采购计划法Vendor Scheduling工作中心能力Work Center Capacity工作中心Work Center在制品Work in Process工作令跟踪Work Order Tracking工作令Work Order工作进度安排Work Scheduling零库存Zero Inventories经济订货批量＝Squat(2*年订货量*平均一次订货准备所发生成本/每件存货的年储存成本)美国生产和库存控制协会APICS，American Production & Inventory Control Society。

供应链英文专业词汇

ABC clasification ABC分类Acceptable Quality Level 允许水准Accessory 附件Action Report 行动报告Add/Delete BOM 增删材料表Aggregate Planning总体规划Agile Manufacturing 灵活制造Allocated Inventory 保留量Allowance 宽放Alternate BOM 替代材料表American Production and Inventory Control Society 美国产业管理学会Anticipation Inventory 预期库存Assemble to Order 定单组装Automation 自动化Autonomation 自主化Available Inventory 可用库存Available to Promise 可答应量BackFlush 倒冲入账Backlog 待交货Back Order 逾期定单Bill of Labor 人力表Bill of Material 材料表bill of resources 资源表BOM Code 材料表码Bom Explosion 材料表展开Bom Implosion 材料表逆展Bom Structure 材料表结构Budgeted Capacity 预算产量Built-on-the-line parts 线上生产零件Bulk Issue 大批发料Bullwhip Effect 长鞭效应Business Plan 事业计划Business Process Reengineering 企业程序再造Capacity 产量Capacity Control 产量控制Capacity requirement planning产量需求规划Check-in 结入Check-Out 结出Client/ Server Architecture 主从式架构Common part Bom 共享件材料表Computer aided design system 计算机辅助设计系统connected flow 相连材料流consolidated freight 合并货运constraint management 限制因素管理continuous improvement 连续改善continuous production 连续式生产critical capacity 关键产量critical part 关键零件customer order 客户定单customization 客制化customer service level 顾客服务水准cycle count interval 周期盘点区间cycle counting 周期盘点cycle time 周期时间customer relationship management 客户关系管理data flow diagram 数据流程图de-coupling stock 反耦合库存Demand Management 需求管理Demand Rate 需求速率Demand time fence 需求时栅demonstrated capacity 验证产量dependent demand 依赖需求diagnostic test 诊断测试disconnected flow 分离材料流iscret manufacturing 装配式生产distribution center 配销中心distribution requirement planning配销需求规划drum-buffer-rope control DBR管制法earliest start date 最早开工日economic order quantity 经济订购量economic part period 经济量期elimination,combination,rearrangement,simplification ECRS改善法emergency kanban 紧急看板employee empowerment 员工授权employee involvement 员工参与end user computing 使用者自建系统engineering to order 定单设计engineering change 设计变更engineering product structure 工程用产品结构表exception report 企业资料规划enterprise resource planning例外报告executive information system 主管信息系统existence test 存在测试expeditor 催料人员final assembly schedule 最终组装排程finished goods 完成品firm planned order 固定计划定单first in first out 先进先出fixed order quantity 定量批量法flow shop 流程生产工厂forecast 预测forecast horizon预测期间fundamental data 基本资料gateway workstation 投料工作站graphic user interface 图形接口gross requirement 总需求group technology 群组技术hedge inventory 避险库存inbound queue control 输入端队列控制independent demand 独立需求input/output control 输入/输出控制intermittent production 间歇式生产inventory management 库存管理inventory status 库存状态inventory sub-type 库存副型态inventory type 库存型态item 材料(项目)item master 材料主档job 工件,工作job shop 工件生产工厂joint operation 联合作业just in time 及时供补kanban 看板kanban ceiling 看板界限latest start date 最晚开工日lead time 前置时间lead time offset 前置时间冲销least total cost 最低总成本批量法least unit cost 最低单位成本批量法level scheduling 平准化排程level production(linearity) 平准化生产linearity 定率生产load 负荷look ahead/look back 瞻前顾后法lot for lot 逐批批量法lot number 批号lot size 批量lot size inventory 批量库存lot sizing rule 批量法则low-level code 最低阶码maintenance, repair and operational supplies 间接物料make to order 定单生产make to stock 计划生产managerial product structure 管理用产品结构表manufacturing bom 制造单元manufacturing cell 制令单manufacturing order 制造规划与控制manufacturing planning and control 制造资源规划master production scheduling 主生产排程master scheduler 主生产排程员material service sheduling 主服务排程material handling 材料搬运material requirement planning材料需求规划(计划) mean absolute deviation 平均绝对差modular bom 模块材料表modular production 模块化生产mps item MPS 项目mrp crusades MRP 改革运动mrp nervousness MRP不安定性multilevel mps 多阶主生产排程net change 净变法net requirement 净需求offset time 冲销时间one less at a time 一次减一点on-hand inventory 在库量on-order inventory 在途量open system platform 开放系统平台operations planning and control 作业规划与管制operations process chart 作业程序图option 选用件optional bom 选用材料件order interval 订购区间order point 订购点original equipment manuafacturer 原设备制造商outbound queue control 输出端队列控制overflow stockroom 溢量仓parent/component 父件/子件part number 件号part periodic balancing 量期平衡批量法past due 逾期量peg file 溯源文件pegging 溯源period length 期长periodic order quantity 定期批量法periodic review system 定期评估法phantom 幽灵材料phantom bom 幽灵材料表phantom component 幽灵子件picking order 领料单pipeline stock 管路库存plan-do-check-action cycle 计划-执行-检查-行动循环planned order receipts 计划定单收料planned order releases 计划定单发出planning bom 计划材料表planning horizon计划期间planning time fence 计划时栅point of use 使用点primary stockroom 基本仓priority control 优先次序控制priority planning优先次序规划preventive maintenance 预防性维护process flow chart 制程流程图process manufacturing 流程式生产product configuration system 产品构造系统product family 产品族product line 产品线product load profile 产品负荷表product structure 产品结构表product sub-line 产品副线production activity control 生产活动管制production rate 生产速率production plan 生产计划production planning生产规划production run 生产连project-based production 项目式生产projected available balance 预计可用量projected on-hand 预计在库量pseuo bom 假材料表pull signal 拉式讯号pull system 拉式系统purchase order 订购单purchase reuisition 请购单quantity-per 单位用量quick response 快速反应rated capacity 评估产量raw material 原材料reasonableness test 合理测试receiving order 收料单refill kanban 补充看板regeneration 再生法re-order point 再订购点法repetitive manufacturing 重复性生产replacement part 替代件replenishment plan 补充计划replenishment time 补充时间resource profile 资源负荷表resource requirement planning资源需求规划rework kanban 重加工看板rolling kanban 滚动看板rolling schedule 滚动式排程rough-cut capacity planning粗略产量规划route 途程routing 途程表safety stock 安全存量safety time 安全时间safety order 销售定单scheduled receipts 在途量(已订未交量) scarp rate 报废率secondary stockroom 次级仓semi-finished goods 半成品serial number 序号setup 准备作业shop calendar 厂历shop floor 制造现场shop floor control 制造现场控制shop order 制令单significant numbering 显义编号standard coefficient 标准系数stock keeping unit 材料库存单位subsontract order 外包单super bom 超材料单supply chain management 供应链管理synchronized control 同步控制synchronized production 同步生产theoretical capacity 理论产量theory of constraints 限制理论three tier architecture 三层式架构throughtput 产出率time bucket 时段time phased order point 分期间订购点法transferring order 调拨单transportation inventory 运输库存total employee involvement 全员参与total preventive maintenance 全面预防性维护total productive maintenance 全面生产性维护total quality management 全面质量管理two bin system 双箱法two level mps 双阶主生产排程unit of measure 单位visual review system 目视评估法where-used report 用途表WIP inventory 在制品库存WIP tracking 在制品追踪work flow control system 工作流程控制系统work-in-process 在制品yield 良品率Common Terms in Newspapers ：accredited journalist n. 特派记者advertisement n．广告．advance n．预发消息；预写消息affair n．桃色新闻；绯闻anecdote n．趣闻轶事assignment n．采写任务attribution n. 消息出处，消息来源back alley news n. 小道消息backgrounding n．新闻背景Bad news travels quickly. 坏事传千里。

精益生产之补充拉系统Replenishment_Pull_Systems(中英文对照)

Measure
Analyze Improve
Replenishment Pull Systems
Manufacturing Pull Systems
Purchase Pull Systems Buffer Locations
Control

Pull System Platforms
Manual vs. Electronic Pull Systems: Handling Seasonality

绩效量测
附录
RD010402
Lean Six Sigma Improvement Process Road Map
Define
• • •
Improve
Measure
• • • •
Analyze
• • • • •
Improve
• • • • •
Control
• • • • • • •
• •
Identify Problem Develop List of Customers Develop List of CTQ’s from Voice of the Customer Finalize Project Focus and Key Metrics Complete PDF
采购零件
成品
通过缓冲库存将供应过程和消耗过程联系起来. 零件的补充是基于消耗的实际需求而被触发的.
未来需求帮助确定缓冲量的大小，而不是决定于实际
材料的发放.
Replenishment Pull Systems
9
Replenishment Pull System Benefits

启发式 FF-NN 模型在随机动态批量问题中的应用

启发式 FF-NN 模型在随机动态批量问题中的应用鲜敏;郑翔【摘要】For the mathematical intractability caused by the complex structure of multi-period single-item lot sizing problem under stochastic environment,we propose the heuristic-based feed forward neural networks (FF-NN)model.By studying an optimal lot sizing strategy which is based on the minimum total relevant cost price and uncertainty demand,we construct four FF-NN models,they are based on Taguchi method,back propagation (BP),genetic algorithm (GA)and bee colony algorithm(BA)respectively.We also compare all combinations of various methodsand models by using heuristic cost calculation methods in three specific domains,including the revised silver and meal (RSM),revised least unit cost (RLUC)and cost benefit (CB).Experimental results show that the combination of BA-based FF-NN model and RLUC method is the heuristic combination to help the decision makers the best,which well settles the mathematical intractability of stochastic dynamic lot-sizing problem.%针对随机环境下多期单项批量问题的复杂结构导致的数学难解性，提出基于启发式的前馈神经网络 FF-NN（Feed For-ward-Neural Network）模型。

不确定交货条件下两供应商-单制造商协同供货模型

不确定交货条件下两供应商-单制造商协同供货模型李果;马士华;高韬;王兆华【摘要】Uncertainties in supply, manufacturing or customer demand can disrupt supply chain operations. However, supply chain disruption caused by manufacturing and demand uncertainties have not been extensively studied in the current literature. The increase of social and natural disasters, such as 9/11 terrorist attack in the USA and earthquake in Japan, are raising the importance of studying supply chain uncertainty and disruption.This study focuses on random yields as an important aspect of supply uncertainty. In the multi-suppliers and one-manufacturer system with random yields and JIT delivery, each supplier will optimize its production quantity according to manufacturer' s order quantity. The quantity delivered by different suppliers may be mismatched due to random yields. Delivery quantity of suppliers can lead to mismatching of component quantity in an assembly system. As a result of the mismatching issue, overstock and out-of-stock problems in stores can occur frequently. Therefore, it is essential to design an effective strategy to streamline the operation of multi-suppliers and one-manufacturer system.We construct a mathematical model for the two-supplier and one-manufacturer coordination based on penalty policy under delivery uncertainty. The model is to address the following questions: (1) How will residual value influence the decision of a manufacturer if order quantity is larger than delivery quantity? (2) How will inventory costs influence the decision ofmanufacturer and supplier due to uncertain delivery quantity of suppliers? And (3) How to use penalty cost to influence the supply chain coordination? The model is constructed based on the assumptions of risk neutral, information symmetry, fully rational decision makers, assembly of proportional parts, and JIT delivery.Our findings show that the expected profit function of suppliers is the concave function of production quantity. There is only one best production quantity to maximize the expected profit of suppliers. Given the order quantity of a manufacturer, there exists a unique Nash equilibrium between two suppliers' game in the decentralized decision mode. If order quantity, purchase price, inventory cost and unit production cost are predetermined, the unit penalty cost can indirectly influence the optimal expected profit function of suppliers. The expected profit function of manufacturers is a concave function of order quantity and there exists only one best order quantity to maximize the expected profit of suppliers. Furthermore, a manufacturers optimal order quantity is larger than or equal to the market demand.In summary, under the condition of delivery uncertainty caused by random yields of suppliersthe two-supplier and one-manufacturer system can adopt our proposed penalty mechanism to coordinate supply chain operations. The mechanism ensures that a manufacturer can adjust the order quantity and unit component penalty cost to maximize the profit for the entire supply chain.%研究了不确定交货条件下两供应商-单制造商系统基于惩罚策略的协同供货模型.首先建立了基于惩罚策略的供应商和制造商模型,接着对模型中供应商和制造商的最优决策进行了分析,得出两供应商的最优生产批量决策存在唯一纳什均衡,且最优期望利润间接地受其单位缺货成本的影响,而制造商最优订货批量大于等于市场需求.其次建立了集中决策下供应链利润函数模型,并证明了该函数是决策变量的联合凹函数,存在唯一的最优解.为了达到集中决策下的供应链利润曩大化,推导出基于惩罚策略的供应链协同机制.制造商通过调整订货批量和零件单位缺货惩罚成本,在供应链利润最大化前提下获得最大的剩余利润.最后数例分析说明制造商如何通过控制该惩罚成本系数使得供应链协同并获得最大的剩余利润.【期刊名称】《管理工程学报》【年(卷),期】2011(025)003【总页数】9页(P91-99)【关键词】不确定交货;惩罚成本;纳什均衡;供应链协同;集中决策【作者】李果;马士华;高韬;王兆华【作者单位】北京理工大学管理与经济学院,北京100081;华中科技大学管理学院,湖北武汉430074;河北省电子信息产品监督检验院,河北石家庄050071;北京理工大学管理与经济学院,北京100081【正文语种】中文【中图分类】F273.7一直以来，供应链运作存在供应、制造和需求的三大不确定性，而人们一直比较关注制造和需求不确定性对供应链运作的影响［1］。

APICS(DSP)课堂笔记整理

Complete Order Point Model OP = DDLT (Demand during lead time) + SS (Safety Stock)

Periodic Review System Max Inventory= D×(R+L)+SS
批注 [CJL22]: D 是指月耗量，R 是指 review periodic 频率， L 是指采购在途周期批注 [CJL23]: RP 资源计划，RCCP 粗能力计划，CRP 能力需求计划
批注 [CJL10]: MRO 不是资产而是费用

Typen Average cost Standard cost Actual cost Transfer cost First in, first out(FIFO) Last in, first out(LIFO)
accounts payable days

Customer Service Metrics
Item inventory policies Lot-sizing decision factors Order quantity constraints and modifiers Order quantities in production and service Environments Fulfillment strategy Make-to-stock XYZ 订单稳定性分析+存货 ABC 分类 Make-to-order Assemble-to-order Engineer-to-order

Inventory Turns =
Average inventory in dollars

顾问必须熟悉ERP专业术语中英及缩写对照

参谋必须熟悉的ERP专业术语-中英及缩写对照常见erp名词术语解释,中英文对照：ERP (enterprise resource planning) 企业资源方案CRM (customer relational management) 客户关系管理BPR (business process reengineering) 企业业务流程重组BOM (bill of material) 物料清单JIT (Just-in-Time) 准时制生产TQM (total quality management) 全面质量管理MRPII(Manufacturing Resource Planning) 制造资源方案，为了与物料需求方案MRP区别，后面加了twoMRP (material requiremants planning) 物料需求方案A字母acquisition cost,ordering cost 定货费assemble-to-order 定货组装AS/RS (automated storage/retrieval system) 自动化仓储系统activity cost pool 作业本钱集activity-based costing 作业基准本钱法ATP (available to promise) 可供销售量APICS (American Production and Inventory Control Society,Inc.)美国生产与库存管理系统agile manufacturing 敏捷制造APICS Applied Manufacturing Education Series 实用制造管理系列培训教材AMT (Advanced Manufacturing Technology) 先进制造技术anticipation inventory 预期储藏B字母back scheduling 倒排方案backflushing 反冲法BOR (bill of resource) 资源清单business plan 经营规划batch process 批流程bottleneck 瓶颈资源(工序)back order 脱期定单backlog 拖欠定单bill of materials 物料清单bucketless system 无时段系统C字母carrying cost 保管费closed-loop MRP 闭环MRPcost roll-up 本钱滚动计算法costed BOM 本钱物料单cost of stockout 短缺损失critical work center 关键工作中心critical path method 关键路线法CAPP (puter-aided process planning) 计算机辅助工艺设计CASE (puter-aided software engineering) 计算机辅助软件工程CAD 〔puter-aided design〕计算机辅助设计CAM (puter-aided manufacturing) 计算机辅助制造CIMS (puter integrated manufacturing system) 计算机集成制造系统critical ratio 紧迫系数ponent 子件/组件cost driver rate 作业本钱发生因素单位费用cost driver 作业本钱发生因素customer deliver leadtime 客户交货提前期cumulative lead time 累计提前期continuous process 连续流程MS (Customer Oriented Manufacturing Management System) 面向客户制造管理系统capacity level 能力利用水平capacity management 能力管理capacity requirements planning 能力需求方案current standart cost 现行标准本钱cycle counting 循环盘点D字母DCS (distributed control system) 分布式控制系统DMRP (distributed MRP) 分布式MRPDRP (distribution resource planning) 分销资源方案distribution requirements planning 分销需求方案demonstrated capacity 纪实能力decision support system 决策支持系统discrete manufacturing 离散型生产dispatch list 派工单days offset 偏置天数dependent demand 相关需求件demand management 需求管理DTF (demand time fence) 需求时界demand cycle 需求周期drop shipment 直运E字母EDI (electronic datainterchange) 电子数据交换ergonomics 工效学EOQ (economic order quantity) 经济定货量法earliest due date 最早定单完成日期ECO (engineering change order/notice) 设计变更通知engineering BOM 工程物料清单ETO (engineer-to-order) 专项生产F字母financial accounting 财务会计financial entity 财务实体fixed period requirements 定期用量法FOQ (fixed order quantity) 固定批量法formal system 规X化管理系统feature 根本组件floor stock,bulk item 作业现场库存firm-planned order 确定定单firm-planned time fence 确定方案时单FMS (flexible manufacturing system) 柔性制造系统feature 特征件forward scheduling 顺排方案favorable variance 有利差异FCS (finite capacity scheduling) 有限能力方案finite forward scheduling 有限顺排方案finite loading 有限排负荷FAS (final assembly schedule) 总装进度G字母GT (group technology) 成组技术gross requirements 毛需求H字母hedge inventory 囤积库存I字母independent demand 独立需求informal system 非规X化管理inventory 库存inventory turnover/turns 库存(资金)周转次数indented BOM 缩排式物料清单input/output control 投入/产出控制item,material,part 物料item master,material master 物料主文件infinite loading 无限排负荷J字母job shop 机群式布置车间K字母kitting 配套出售件L字母low-lever code 低层码logistics 后勤保证体系lean production 精益生产least slack per operation 最小单个工序平均时差lot sizing 批量规那么lot size inventory 批量库存lead time 提前期lead time offset 提前期偏置lot-for-lot 因需定量法live pilot 应用模拟M字母move time 传送时间MTO (make-to-order) 定货生产management accounting 管理会计MIS (management information system) 管理信息系统minimum balance 最小库存余量management by exception 例外管理法modular BOM 模块化物料单measure of velocity 生产速率水平maintenance,repair,and operation supplies 维护修理操作物料material management 物料管理material review board 物料核定机构material manager 物料经理material available 物料可用量Modern Materials Handling 现代物料搬运manufacturing BOM 制造物料清单MES (manufacturing executive system) 制造执行系统MPS (master production schedule) 主生产方案master scheduler 主生产方案员N字母net change 净改变法net requirements 净需求netting 净需求计算O字母order policy 定货策略order point system 定货点法ordering cost 定货费overhead apportionment/allocation 间接费分配overhead rate,burden factor,absorption rate 间按费率option 可选件open order 未结定单OPTO (ptimized Production Technology) 优化生产技术P字母ploicy and procedure 工作准那么与工作规程planned order receipts 方案产出量planned order 方案定单planned capacity 方案能力PERT (program evaluation research technology) 方案评审技术planning horizon 方案期PTF (planned time fence) 方案时界planned order releases 方案投入量planning BOM 方案物料单proposed cost 建议本钱picking list 领料/提货单parent item 母件Pareto Principle 帕拉图原理production cycle 生产周期production activity control 生产作业控制point of use 使用点phantom 虚拟件performance measurement 业绩评价projected available balance 预计可用库存量priority 优先级prototyping,puter pilot 原型测试POQ (period order quantity) 周期定货量法pegging 追溯Q字母queue time 排队时间R字母resupply order 补库单RCCP (rough-cut capacity planning) 粗能力方案repetitive manufacturing 重复式生产rated capacity 额定能力routing 工艺路线run time 加工时间resource requirements planning 资源需求方案requisition 请购单regeneration 全重排法released order ,open order 下达定单required capacity 需用能力S字母safety stock 平安库存safety lead time 平安提前期standard cost system 标准本钱体系shop floor control 车间作业控制shop order 车间定单scrap 废品率scheduler 方案员supply chain 供需链shop calendar 工作日历summarized BOM 汇总物料清单scheduled receipts 方案接收量seasonal stock 季节储藏ship-to 交货地set up time 准备时间spending variance,expenditure variance 开支差异SMED (single-minute exchange of dies) 快速换模法simulated cost 模拟本钱shrinkage 缩减率synchronous manufacturing 同步制造SOP (sales and operations planning) 销售与动作规划T字母transit time 传送时间top management mitment 领导承诺time bucket 时段time fence 时界time zone 时区TOC (Theory of Constraints) 约束理论transportation inventory,pipeline stock 在途库存total lead time 总提前期U字母V字母volume variance 产量差异value chain 价值链virtual organization 虚拟企业value-added chain 增值链W字母work order 车间定单wait time 等待时间work flow 工作流work center 工作中心world class manufacturing excellence 国际优秀制造业what-if 如果怎样-将会怎样X字母Y字母yield 成品率。

生产术语

英文术语中文术语ABC clasification ABC分类Acceptable Quality Level 允许水准Accessory 附件Action Report 行动报告Add/Delete BOM 增删材料表Aggregate Planning 总体规划Agile Manufacturing 灵活制造Allocated Inventory 保留量Allowance 宽放Alternate BOM 替代材料表American Production and Inventory Control Society 美国产业管理学会Anticipation Inventory 预期库存Assemble to Order 定单组装Automation 自动化Autonomation 自主化Available Inventory 可用库存Available to Promise 可答应量BackFlush 倒冲入账Backlog 待交货Back Order 逾期定单Bill of Labor 人力表Bill of Material 材料表bill of resources 资源表BOM Code 材料表码Bom Explosion 材料表展开Bom Implosion 材料表逆展Bom Structure 材料表结构Budgeted Capacity 预算产量Built-on-the-line parts 线上生产零件Bulk Issue 大批发料Bullwhip Effect 长鞭效应Business Plan 事业计划Business Process Reengineering 企业程序再造Capacity 产量Capacity Control 产量控制Capacity requirement planning 产量需求规划Check-in 结入Check-Out 结出Client/ Server Architecture 主从式架构Common part Bom 共享件材料表Computer aided design system 计算机辅助设计系统connected flow 相连材料流consolidated freight 合并货运constraint management 限制因素管理continuous improvement 连续改善continuous production 连续式生产critical capacity 关键产量critical part 关键零件customer order 客户定单customization 客制化customer service level 顾客服务水准cycle count interval 周期盘点区间cycle counting 周期盘点cycle time 周期时间customer relationship management 客户关系管理data flow diagram 数据流程图de-coupling stock 反耦合库存Demand Management 需求管理Demand Rate 需求速率Demand time fence 需求时栅demonstrated capacity 验证产量dependent demand 依赖需求diagnostic test 诊断测试disconnected flow 分离材料流iscret manufacturing 装配式生产distribution center 配销中心distribution requirement planning 配销需求规划drum-buffer-rope control DBR管制法earliest start date 最早开工日economic order quantity 经济订购量economic part period 经济量期elimination,combination,rearrangement,simplification ECRS改善法emergency kanban 紧急看板employee empowerment 员工授权employee involvement 员工参与end user computing 使用者自建系统engineering to order 定单设计engineering change 设计变更engineering product structure 工程用产品结构表exception report 企业资料规划enterprise resource planning 例外报告executive information system 主管信息系统existence test 存在测试expeditor 催料人员final assembly schedule 最终组装排程finished goods 完成品firm planned order 固定计划定单first in first out 先进先出fixed order quantity 定量批量法flow shop 流程生产工厂forecast 预测forecast horizon 预测期间fundamental data 基本资料gateway workstation 投料工作站graphic user interface 图形接口gross requirement 总需求group technology 群组技术hedge inventory 避险库存inbound queue control 输入端队列控制independent demand 独立需求input/output control 输入/输出控制intermittent production 间歇式生产inventory management 库存管理inventory status 库存状态inventory sub-type 库存副型态inventory type 库存型态item 材料(项目)item master 材料主档job 工件,工作job shop 工件生产工厂joint operation 联合作业just in time 及时供补kanban 看板kanban ceiling 看板界限latest start date 最晚开工日lead time 前置时间lead time offset 前置时间冲销least total cost 最低总成本批量法least unit cost 最低单位成本批量法level scheduling 平准化排程level production(linearity) 平准化生产linearity 定率生产load 负荷look ahead/look back 瞻前顾后法lot for lot 逐批批量法lot number 批号lot size 批量lot size inventory 批量库存lot sizing rule 批量法则low-level code 最低阶码maintenance, repair and operational supplies 间接物料make to order 定单生产make to stock 计划生产managerial product structure 管理用产品结构表manufacturing bom 制造单元manufacturing cell 制令单manufacturing order 制造规划与控制manufacturing planning and control 制造资源规划master production scheduling 主生产排程master scheduler 主生产排程员material service sheduling 主服务排程material handling 材料搬运material requirement planning 材料需求规划(计划) mean absolute deviation 平均绝对差modular bom 模块材料表modular production 模块化生产mps item MPS 项目mrp crusades MRP 改革运动mrp nervousness MRP不安定性multilevel mps 多阶主生产排程net change 净变法net requirement 净需求offset time 冲销时间one less at a time 一次减一点on-hand inventory 在库量on-order inventory 在途量open system platform 开放系统平台operations planning and control 作业规划与管制operations process chart 作业程序图option 选用件optional bom 选用材料件order interval 订购区间order point 订购点original equipment manuafacturer 原设备制造商outbound queue control 输出端队列控制overflow stockroom 溢量仓parent/component 父件/子件part number 件号part periodic balancing 量期平衡批量法past due 逾期量peg file 溯源文件pegging 溯源period length 期长periodic order quantity 定期批量法periodic review system 定期评估法phantom 幽灵材料phantom bom 幽灵材料表phantom component 幽灵子件picking order 领料单pipeline stock 管路库存plan-do-check-action cycle 计划-执行-检查-行动循环planned order receipts 计划定单收料planned order releases 计划定单发出planning bom 计划材料表planning horizon 计划期间planning time fence 计划时栅point of use 使用点primary stockroom 基本仓priority control 优先次序控制priority planning 优先次序规划preventive maintenance 预防性维护process flow chart 制程流程图process manufacturing 流程式生产product configuration system 产品构造系统product family 产品族product line 产品线product load profile 产品负荷表product structure 产品结构表product sub-line 产品副线production activity control 生产活动管制production rate 生产速率production plan 生产计划production planning 生产规划production run 生产连project-based production 项目式生产projected available balance 预计可用量projected on-hand 预计在库量pseuo bom 假材料表pull signal 拉式讯号pull system 拉式系统purchase order 订购单purchase reuisition 请购单quantity-per 单位用量quick response 快速反应rated capacity 评估产量raw material 原材料reasonableness test 合理测试receiving order 收料单refill kanban 补充看板regeneration 再生法re-order point 再订购点法repetitive manufacturing 重复性生产replacement part 替代件replenishment plan 补充计划replenishment time 补充时间resource profile 资源负荷表resource requirement planning 资源需求规划rework kanban 重加工看板rolling kanban 滚动看板rolling schedule 滚动式排程rough-cut capacity planning 粗略产量规划route 途程routing 途程表safety stock 安全存量safety time 安全时间safety order 销售定单scheduled receipts 在途量(已订未交量) scarp rate 报废率secondary stockroom 次级仓semi-finished goods 半成品serial number 序号setup 准备作业shop calendar 厂历shop floor 制造现场shop floor control 制造现场控制shop order 制令单significant numbering 显义编号standard coefficient 标准系数stock keeping unit 材料库存单位subsontract order 外包单super bom 超材料单supply chain management 供应链管理synchronized control 同步控制synchronized production 同步生产theoretical capacity 理论产量theory of constraints 限制理论three tier architecture 三层式架构throughtput 产出率time bucket 时段time phased order point 分期间订购点法transferring order 调拨单transportation inventory 运输库存total employee involvement 全员参与total preventive maintenance 全面预防性维护total productive maintenance 全面生产性维护total quality management 全面质量管理two bin system 双箱法two level mps 双阶主生产排程unit of measure 单位visual review system 目视评估法where-used report 用途表WIP inventory 在制品库存WIP tracking 在制品追踪work flow control system 工作流程控制系统work-in-process 在制品yield 良品率。

外资公司日常管理英语常用词汇

外资公司日常管理英语常用词汇一、组织机构及职位总经理办公室General manager’s office企管部 Enterprise management department (EM)行政部 Administration department (AD)销售部 Sales department (SD)财务部 Financial department (FD)技术部 Technology department (TD)物控部 Production material control department (PMC)生产部 Production department(PD)模具部 Mold manufacturing department, Tooling manufacturing department 品管部 Quality Assurance department (QA)冲压车间 Stamp workshop, press workshop注塑车间 injection workshop装配车间 Assembly workshop模具装配车间 Mold and die Assembly workshop金属加工车间 Metal machine workshop电脉冲车间 Electric discharge process workshop线切割车间 Wire cutting process workshop工磨车间 Grinding workshop总经理 General manager (GM)副总经理 Vice-general manager经理 Manager董事长 President副董事长 Vice-presidentXX部门经理 Manager of XX department 主任、主管 supervisor拉长 Line leader组长 Foreman, forelady秘书 secretary文员 clerk操作员 operator助理 assistant职员 staff二、产品连接器 connector端子 terminal条型连接器 bar connector阴连接器 Housing阳连接器 wafer线束 wire harness间距 space额定电压 rated voltage额定电流 rated current接触电阻 contact resistance绝缘电阻 insulation resistance超声波焊接 ultrasonic welding 耐压 withstand voltage 针 pin物料编号 part number导线 wire基体金属 Base metal电缆夹 cable clamp倒角 chamfer接触面积 contact area接触件安装孔 contact cavity接触长度 contact length接触件电镀层 contact plating接触压力 contact pressure 接触件中心距 contact space接触簧片 contact spring接触孔 socket contact法兰、凸缘 Flange界面间隙 interfacial gap键 Key键槽 keyway过渡段 ramp屏蔽套 shielding定位基准 Datum reference扁平电缆 flat cable ，Ribbon cable柔性印刷电线 Flexible printed wiring多层印制电路 Multilayer printed circuit焊盘 pad图形 pattern间距 pitch负极 Negative pole正极 positive pole回流 Reflow原理图 Schematic diagram单面板 single sided board双面板 Two-sided board，Double-sided board 表面安装 surface Mounting翘曲 warp， bow波峰焊 wave soldering编织层 braid同轴电缆 coaxial cable电介质 dielectric电缆中导线的头数ends外部干扰 external interference填充物 filler护套 jacket比重 specific gravity电阻的温度系数Temperature coefficient of resistance三、模具塑料模具 mould of plastics注塑模具 injection mould冲压模具 die模架 mould base定模座板 Top clamping plate /Top plate /Fixed clamp plate 水口推板 stripper plateA板 A plateB板 B plate支承板 support plate方铁 spacer plate底针板 ejector plate面针板 ejector retainer plate回针 Return pin导柱 Guide pin有托导套 Shoulder Guide bush直导套 Straight Guide bush动模座板Bottom clamp plateMoving clamp plate基准线datum line基准面datum plan型芯固定板core-retainer plate 凸模固定板punch-retainer plate 顶针ejector pin单腔模具single cavity mould多腔模具multi-cavity mould多浇口multi-gating浇口gate缺料starving排气breathing光泽gloss合模力mould clamping force锁模力mould locking force挤出extrusion开裂crack循环时间cycle time老化aging螺杆screw麻点pit嵌件insert活动镶件movable insert起垩chalking浇注系统feed system主流道 sprue分流道runner浇口gate直浇口direct gate , sprue gate轮辐浇口spoke gate , spider gate点浇口pin-point gate测浇口edge gate潜伏浇口submarine gate , tunnel gate 料穴cold-slug well浇口套sprue bush流道板runner plate排飞槽vent分型线（面）parting line定模stationary mould，Fixed mould动模movable mould, movable half上模upper mould， upper half下模lower mould， lower half型腔cavity凹模cavity plate，cavity block拼块split 定位销dowel定位销孔dowel hole型芯core斜销angle pin, finger cam滑块slide滑块导板slide guide strip楔紧块heel block, wedge lock拉料杆sprue puller定位环locating ring冷却通cooling channel脱模斜度draft滑动型芯slide core螺纹型芯threaded core热流道模具hot-runner mould绝热流道模insulated runner mould熔合纹weld line （flow line）三板式模具three plate mould脱模ejection脱模剂release agent注射能力shot capacity注射速率injection rate注射压力injection pressure差色剂colorant保压时间holdup time闭模时间closing time定型装置sizing system阴模female mould，cavity block阳模male mould电加工设备Electron Discharge Machining数控加工中心CNC machine center万能铁床Universal milling machine卧式刨床Horizontal planer车床Engine lathe平面磨床Surface grinding machine去磁机Demagnetization machine 换模腔模具 interchangeable cavity mould万能摇臂钻床Universal radial movable driller立式钻床Vertical driller超声波清洗机Ultrasonic clearing machine四、品管SPC statistic process control品管保证Quality Assurance品管控制Quality control来料检验IQC Incoming quality control巡检IPQC In-process quality control校对calibration动态试验dynamic test环境试验Environmental test非破坏性试验non-destructive test光泽gloss击穿电压(dielectric) breakdown voltage拉伸强度tensile strength冷热骤变试验thermal shock test环境试验炉Environmental chamber盐雾实验salt spray test绝缘电阻测试验仪Insulating resistance meter内应力internal stress疲劳fatigue蠕变creep试样specimen撕裂强度tear strength缩痕shrink mark, sink mark耐久性durability抽样sampling样品数量sample sizeAQL Acceptable Quality level批量lot size抽样计划sampling planOC曲线operation curve试验顺序sequence of tests 环境温度ambient temperature 可焊性solderability阻燃性flame resistance五、生产注塑机injection machine冲床Punch machine混料机blender mixer尼龙nylon黄铜 brass青铜 bronze紫(纯)铜 copper料斗hopper麻点pit配料compounding涂层coating飞边flash预热preheating再生料reworked material再生塑料reworked plastics工艺设计process design紧急停止emergency stop延时time delay六．物控保质期shelf lifeABC分类法ABC Classification反常需求Abnormal Demand措施信息Action Message活动报告标志Action-report-flag基于活动的成本核算Activity-based Costing (ABC)实际能力Actual Capacity实际成本Actual Costs调整现有库存量Adjust-on-hand已分配量Allocation Alternative Routine 装配订单Assembly Order装配零件表Assembly Parts List装配Assembly计划自动重排Automatic Rescheduling可达到库存Available Inventory可用材料Available Material达到库存Available Stock可利用工时Available Work可签约量Available-to-promise平均库存Average Inventory欠交订单Back Order倒序计划Back Scheduling倒冲法Back flush未完成订单Backlog现有库存余额Balance-on-hand Inventory 批号Batch Number批量生产Batch Production标杆瞄准Benchmarking工时清单Bill of Labor提货单Bill of Lading物料清单Bill of Material分库Branch Warehouse经营规划Business Plan采购员Buyer能力管理Capacity Management能力需求计划Capacity Requirements Planning (CRP)保管费率Carrying Cost Rate保管费Carrying Cost单元式制造Cellular Manufacturing修改批量日期Change Lot Date修改工序Change Route修改产品结构Change Structure检查点Check Point闭环物料需求计划Closed Loop MRP通用工序标识Common Route ID计算机集成制造Computer-integrated Manufacturing (CIM)配置代码Configuration Code约束管理/约束理论Constraints Management/Theory of Constraints (TOC) 依成本的材料清单Costed Bill of Material急需零件Critical Part累计提前期Cumulative Lead Time现有运转时间Current Run Hour现有运转数量Current Run Quantity周期盘点Cycle Counting调整日期Date Adjust有效日期Date Available修改日期Date Changed结束日期Date Closed截止日期Date Due生产日期Date in Produced库存调整日期Date Inventory Adjust作废日期D ate Obsolete收到日期Date Received交付日期Date Released需求日期Date Required发货日期Date to Pull空负荷Dead Load需求管理Demand Management需求Demand实际能力Demonstrated Capacity非独立需求Dependent Demand直接增减库存处理法Direct-deduct Inventory Transaction Processing 发料单Disbursement List派工单Dispatch List分销资源计划Distribution Resource Planning (DRP)鼓－缓冲－绳子Drum-buffer-rope经济订货批量Economic Order Quantity (EOQ)工程变更生效日期Engineering Change Effect Date工程变更生效单Engineering Change Effect Work Order工程停止日期Engineering Stop Date例外控制Exception Control呆滞材料分析Excess Material Analysis急送代码Expedite Code加工订单Fabrication Order补足欠交Fill Backorder总装提前期Final Assembly Lead Time确认的计划订单Firm Planned Order固定订货批量F ixed Order Quantity集中预测Focus Forecasting完全跟踪Full Pegging通用生产管理原则Generally Accepted Manufacturing Practices 毛需求Gross Requirements在制品库存In Process Inventory独立需求Independent Demand投入/产出控制Input/ Output Control检验标识Inspection ID厂际需求Interplant Demand库存周转率Inventory Carry Rate仓库库位类型Inventory Location Type 库存周转次数Inventory Turnover发送订单Issue Order项目记录Item Record物料项目Item加工车间Job Shop准时制生产Just-in-time (JIT)看板Kanban人工工时Labor Hour最后运输日期Last Shipment Date提前期Lead Time层Level负荷量Load Leveling负荷报告Load Report负荷Load仓位代码Location Code仓位备注Location Remarks仓位状况Location Status按需订货Lot for Lot批量标识Lot ID批量编号Lot Number批量Lot Size低位码Low Level Code机器能力Machine Capacity机器加载Machine Loading外购或自制决策Make or Buy Decision面向订单生产的产品Make-to-order Product面向库存生产的产品Make-to-stock Product制造周期时间Manufacturing Cycle Time制造资源计划Manufacturing Resource Planning (MRP II) 主生产计划Master Production Schedule (MPS)物料成本Material Cost物料发送和接收Material Issues and Receipts物料需求计划Material Requirements Planning (MRP)登陆标志MPS Book Flag MPS多重仓位Multiple Location净改变式MRP N et Change MRP净需求Net Requirements新仓位New Location新组件New Parent新仓库New Warehouse不活动报告No Action Report现有库存量On-hand Balance未结订单Open Order订单输入Order Entry订货点Order Point订货方针Order Policy订货承诺Order Promising订货备注Order Remarks双亲Parent零件清单Part Bills零件批次Part Lot零件编号Part Number零件Part反查Pegging领料单Picking List领料/提货Picking计划订单Planned Order后减库存处理法Post-deduct Inventory Transaction Processing 前减库存处理法Pre-deduct Inventory Transaction Processing 发票价格Price Invoice采购订单价格Price Purchase Order优先计划Priority Planning产品控制Product Control产品线Production Line生产规划编制Production Planning产品率Production Rate产品结构树Production Tree预计可用库存Projected Available Balance采购订单跟踪Purchase Order Tracking已分配量Quantity Allocation仓位数量Quantity At Location欠交数量Quantity Backorder完成数量Quantity Completion需求量Quantity Demand毛需求量Quantity Gross进货数量Quantity In排队时间Queue Time队列Queue重生成式MRP Regenerated MRP重排假设Rescheduling Assumption资源需求计划Resource Requirements Planning 粗能力计划Rough-cut Capacity Planning工艺路线Routing安全库存量Safety Stock保险期Safety Time预计入库量Scheduled Receipt残料率Scrap Factor发送零件Send Part维修件Service Parts发货地址Ship Address发货单联系人Ship Contact发货零件Ship Date发货单Ship Order工厂日历Shop Calendar车间作业管理Shop Floor Control损耗系数Shrinkage Factor标准产品成本Standard Product Cost标准机器设置工时Standard Set Up Hour 标准单位运转工时Standard Unit Run Hour 标准工资率Standard Wage Rate状况代码Status Code库存控制Stores Control建议工作单Suggested Work Order约束理论Theory of Constraints (TOC)时间段Time Bucket时界Time Fence单位成本Unit Cost采购计划员V endor Scheduler采购计划法V endor Scheduling工作中心能力Work Center Capacity工作中心Work Center在制品Work in Process工作令跟踪Work Order Tracking工作令Work Order工作进度安排Work Scheduling零库存Zero Inventories经济订货批量＝Squat(2*年订货量*平均一次订货准备所发生成本/每件存货的年储存成本)美国生产和库存控制协会APICS，American Production & Inventory Control Society。

Geometric Modeling

Geometric ModelingGeometric modeling is a crucial aspect of computer-aided design and manufacturing, playing a fundamental role in various industries such as engineering, architecture, and animation. It involves the creation of digital representations of objects and environments using mathematical and computational techniques. This process enables designers and engineers to visualize, simulate, and analyze complex structures and shapes, leading to the development ofinnovative products and solutions. In this discussion, we will explore the significance of geometric modeling from different perspectives, considering its applications, challenges, and future advancements. From an engineering standpoint, geometric modeling serves as the cornerstone of product design and development. By representing physical components and systems through digital models, engineers can assess the performance, functionality, and manufacturability of their designs.This enables them to identify potential flaws or inefficiencies early in thedesign process, leading to cost savings and improved product quality. Geometric modeling also facilitates the creation of prototypes and simulations, allowing engineers to test and validate their ideas before moving into the production phase. As such, it significantly accelerates the innovation cycle and enhances theoverall efficiency of the product development process. In the field ofarchitecture and construction, geometric modeling plays a pivotal role in the conceptualization and visualization of building designs. Architects leverage advanced modeling software to create detailed 3D representations of structures, enabling clients and stakeholders to gain a realistic understanding of the proposed designs. This not only enhances communication and collaboration but also enables architects to explore different design options and assess their spatialand aesthetic qualities. Furthermore, geometric modeling supports the analysis of structural integrity and building performance, contributing to the creation of sustainable and resilient built environments. In the realm of animation andvisual effects, geometric modeling is indispensable for the creation of virtual characters, environments, and special effects. Artists and animators utilize sophisticated modeling tools to sculpt and manipulate digital surfaces, defining the shape, texture, and appearance of virtual objects. This process involves theuse of polygons, curves, and mathematical equations to create lifelike and dynamic visual elements that form the basis of compelling animations and cinematic experiences. Geometric modeling not only fuels the entertainment industry but also finds applications in scientific visualization, medical imaging, and virtual reality, enriching our understanding and experiences in diverse domains. Despite its numerous benefits, geometric modeling presents several challenges,particularly in dealing with complex geometries, large datasets, and computational efficiency. Modeling intricate organic shapes, intricate details, and irregular surfaces often requires advanced techniques and computational resources, posing a barrier for designers and engineers. Moreover, ensuring the accuracy and precision of geometric models remains a critical concern, especially in applications where small errors can lead to significant repercussions. Addressing these challenges demands continuous research and development in geometric modeling algorithms, data processing methods, and visualization technologies. Looking ahead, the future of geometric modeling holds tremendous promise, driven by advancements in artificial intelligence, machine learning, and computational capabilities. The integration of AI algorithms into geometric modeling tools can revolutionize the way designers and engineers interact with digital models, enabling intelligent automation, predictive analysis, and generative design. This paves the way for the creation of highly personalized and optimized designs, tailored to specific requirements and constraints. Furthermore, the convergence of geometric modeling with virtual and augmented reality technologies opens up new possibilities for immersive design experiences, interactive simulations, and digital twinning applications. In conclusion, geometric modeling stands as a vital enabler of innovation and creativity across various disciplines, empowering professionals to visualize, analyze, and realize their ideas in the digital realm. Its impact spans from product design and manufacturing to architecture, entertainment, and beyond, shaping the way we perceive and interact with the physical and virtual worlds. As we continue to push the boundaries of technology and imagination, geometric modeling will undoubtedly remain at the forefront of transformative advancements, driving progress and unlocking new frontiers of possibility.。

电子商务术语

电子商务术语EB/EC Electronic Business/Electronic Commerce 电子商务EDI Electronic Data Interchange 电子数据交换ISP Internet Service Provider 网络服务提供商ERP Electronic Resource Planning 企业资源计划TCP Transmission Control Protocol 传输控制协议CA Certificate Authority 证书权威(认证) SET Secure Electronic Transfer Protocol 安全电子交易协议IAP Internet Access Provider Internet接入服务提供提供商IPP Internet Presence Provider Internet平台服务提供商ICP Internet Content Provider Internet内容服务商IP Internet Protocol 网际协议DNS Domain Name system 域名管理系统LAN local Area Network 局域网WAN Wide Area Network 广域网MAN Metropolitan Area Network 城域ERP Electronic Resource Planning 企业资源计划中的E代表EnterpriseMPS：Master Production Schedule 主生产计划，用于确定每一具体的最终产品在每一具体时间段内生产数量的计划。

（比较不常见）MRP：Material resourse planning 物料资源计划JIT：Just In Time 准时生产EFT：Electronic Fund Transfer 电子资金调拨Data Warehouse 数据仓库（无缩写形式）SCM：Supply Chain Management 供应链管理CRM：Customer Relation Management 客户关系管理EBPP:electronic bill presentment and payment 电子帐单付与支付VPN:virtual private network 虚拟专用网FAQ:frequently asked question 经常提到的问题R&D: research and development 研究与开发PDA:personal digital assistant 个人数字助理ERP (enterprise resource planning) 企业资源计划CRM (customer relational management) 客户关系管理BPR (business process reengineering) 企业业务流程重组BOM (bill of material) 物料清单JIT (Just-in-Time) 准时制生产TQM (total quality management) 全面质量管理MRP2 (Manufacturing Resource Planning) 制造资源计划为了与物料需求计划MRP区别，后面加了twoMRP (material requiremants planning) 物料需求计划A字母acquisition cost,ordering cost 定货费assemble-to-order 定货组装AS/RS (automated storage/retrieval system) 自动化仓储系统activity cost pool 作业成本集activity-based costing 作业基准成本法ATP (available to promise) 可供销售量APICS (American Production and Inventory Control Society,Inc.) 美国生产与库存管理系统agile manufacturing 敏捷制造APICS Applied Manufacturing Education Series 实用制造管理系列培训教材AMT (Advanced Manufacturing Technology) 先进制造技术anticipation inventory 预期储备B字母back scheduling 倒排计划backflushing 反冲法BOR (bill of resource) 资源清单business plan 经营规划batch process 批流程bottleneck 瓶颈资源(工序)back order 脱期定单backlog 拖欠定单bill of materials 物料清单bucketless system 无时段系统C字母carrying cost 保管费closed-loop MRP 闭环MRPcost roll-up 成本滚动计算法costed BOM 成本物料单cost of stockout 短缺损失critical work center 关键工作中心critical path method 关键路线法CAPP (computer-aided process planning) 计算机辅助工艺设计CASE (computer-aided software engineering) 计算机辅助软件工程CAD （computer-aided design）计算机辅助设计CAM (computer-aided manufacturing) 计算机辅助制造CIMS (computer integrated manufacturing system) 计算机集成制造系统critical ratio 紧迫系数component 子件/组件cost driver rate 作业成本发生因素单位费用cost driver 作业成本发生因素customer deliver leadtime 客户交货提前期cumulative lead time 累计提前期continuous process 连续流程COMMS (Customer Oriented Manufacturing Management System) 面向客户制造管理系统capacity level 能力利用水平capacity management 能力管理capacity requirements planning 能力需求计划current standart cost 现行标准成本cycle counting 循环盘点D字母DCS (distributed control system) 分布式控制系统DMRP (distributed MRP) 分布式MRPDRP (distribution resource planning) 分销资源计划distribution requirements planning 分销需求计划demonstrated capacity 纪实能力decision support system 决策支持系统discrete manufacturing 离散型生产dispatch list 派工单days offset 偏置天数dependent demand 相关需求件demand management 需求管理DTF (demand time fence) 需求时界demand cycle 需求周期drop shipment 直运E字母EDI (electronic datainterchange) 电子数据交换ergonomics 工效学EOQ (economic order quantity) 经济定货量法earliest due date 最早定单完成日期ECO (engineering change order/notice) 设计变更通知engineering BOM 工程物料清单ETO (engineer-to-order) 专项生产F字母financial accounting 财务会计financial entity 财务实体fixed period requirements 定期用量法FOQ (fixed order quantity) 固定批量法formal system 规范化管理系统feature 基本组件floor stock,bulk item 作业现场库存firm-planned order 确定定单firm-planned time fence 确定计划时单FMS (flexible manufacturing system) 柔性制造系统feature 特征件forward scheduling 顺排计划favorable variance 有利差异FCS (finite capacity scheduling) 有限能力计划finite forward scheduling 有限顺排计划finite loading 有限排负荷FAS (final assembly schedule) 总装进度G字母GT (group technology) 成组技术gross requirements 毛需求H字母hedge inventory 囤积库存I字母independent demand 独立需求informal system 非规范化管理inventory 库存inventory turnover/turns 库存(资金)周转次数indented BOM 缩排式物料清单input/output control 投入/产出控制item,material,part 物料item master,material master 物料主文件infinite loading 无限排负荷J字母job shop 机群式布置车间K字母kitting 配套出售件L字母low-lever code 低层码logistics 后勤保证体系lean production 精益生产least slack per operation 最小单个工序平均时差lot sizing 批量规则lot size inventory 批量库存lead time 提前期lead time offset 提前期偏置lot-for-lot 因需定量法live pilot 应用模拟M字母move time 传送时间MTO (make-to-order) 定货生产management accounting 管理会计MIS (management information system) 管理信息系统minimum balance 最小库存余量management by exception 例外管理法modular BOM 模块化物料单measure of velocity 生产速率水平maintenance,repair,and operation supplies 维护修理操作物料material management 物料管理material review board 物料核定机构material manager 物料经理material available 物料可用量Modern Materials Handling 现代物料搬运manufacturing BOM 制造物料清单MES (manufacturing executive system) 制造执行系统MPS (master production schedule) 主生产计划master scheduler 主生产计划员N字母net change 净改变法net requirements 净需求netting 净需求计算O字母order policy 定货策略order point system 定货点法ordering cost 定货费overhead apportionment/allocation 间接费分配overhead rate,burden factor,absorption rate 间按费率option 可选件open order 未结定单OPTO (ptimized Production Technology) 优化生产技术P字母ploicy and procedure 工作准则与工作规程planned order receipts 计划产出量planned order 计划定单planned capacity 计划能力PERT (program evaluation research technology) 计划评审技术planning horizon 计划期PTF (planned time fence) 计划时界planned order releases 计划投入量planning BOM 计划物料单proposed cost 建议成本picking list 领料/提货单parent item 母件Pareto Principle 帕拉图原理production cycle 生产周期production activity control 生产作业控制point of use 使用点phantom 虚拟件performance measurement 业绩评价projected available balance 预计可用库存量priority 优先级prototyping,computer pilot 原型测试POQ (period order quantity) 周期定货量法pegging 追溯Q字母queue time 排队时间R字母resupply order 补库单RCCP (rough-cut capacity planning) 粗能力计划repetitive manufacturing 重复式生产rated capacity 额定能力routing 工艺路线run time 加工时间resource requirements planning 资源需求计划requisition 请购单regeneration 全重排法released order ,open order 下达定单required capacity 需用能力S字母safety stock 安全库存safety lead time 安全提前期standard cost system 标准成本体系shop floor control 车间作业控制shop order 车间定单scrap 废品率scheduler 计划员supply chain 供需链shop calendar 工作日历summarized BOM 汇总物料清单scheduled receipts 计划接收量seasonal stock 季节储备ship-to 交货地set up time 准备时间spending variance,expenditure variance 开支差异SMED (single-minute exchange of dies) 快速换模法simulated cost 模拟成本shrinkage 缩减率synchronous manufacturing 同步制造SOP (sales and operations planning) 销售与动作规划T字母transit time 传送时间top management commitment 领导承诺time bucket 时段time fence 时界time zone 时区TOC (Theory of Constraints) 约束理论transportation inventory,pipeline stock 在途库存total lead time 总提前期U字母V字母volume variance 产量差异value chain 价值链virtual organization 虚拟企业value-added chain 增值链W字母work order 车间定单wait time 等待时间work flow 工作流work center 工作中心world class manufacturing excellence 国际优秀制造业what-if 如果怎样-将会怎样X字母Y字母yield 成品率Z字母什么是SCMSupply Chain Management。

供应链相关专业英语词汇

供应链相关专业英语词汇ABC clasification ABC分类Acceptable Quality Level 允许水准Accessory 附件Action Report 行动报告Add/Delete BOM 增删材料表Aggregate Planning总体规划Agile Manufacturing 灵活制造Allocated Inventory 保留量Allowance 宽放Alternate BOM 替代材料表American Production and Inventory Control Society 美国产业管理学会Anticipation Inventory 预期库存Assemble to Order 定单组装Automation 自动化Autonomation 自主化Available Inventory 可用库存Available to Promise 可答应量BackFlush 倒冲入账Backlog 待交货Back Order 逾期定单Bill of Labor 人力表Bill of Material 材料表bill of resources 资源表BOM Code 材料表码Bom Explosion 材料表展开Bom Implosion 材料表逆展Bom Structure 材料表结构Budgeted Capacity 预算产量Built-on-the-line parts 线上生产零件Bulk Issue 大批发料Bullwhip Effect 长鞭效应Business Plan 事业计划Business Process Reengineering 企业程序再造Capacity 产量Capacity Control 产量控制Capacity requirement planning产量需求规划Check-in 结入Check-Out 结出Client/ Server Architecture 主从式架构Common part Bom 共享件材料表Computer aided design system 计算机辅助设计系统connected flow 相连材料流consolidated freight 合并货运constraint management 限制因素管理continuous improvement 连续改善continuous production 连续式生产critical capacity 关键产量critical part 关键零件customer order 客户定单customization 客制化customer service level 顾客服务水准cycle count interval 周期盘点区间cycle counting 周期盘点cycle time 周期时间customer relationship management 客户关系管理data flow diagram 数据流程图de-coupling stock 反耦合库存Demand Management 需求管理Demand Rate 需求速率Demand time fence 需求时栅demonstrated capacity 验证产量dependent demand 依赖需求diagnostic test 诊断测试disconnected flow 分离材料流iscret manufacturing 装配式生产distribution center 配销中心distribution requirementplanning配销需求规划drum-buffer-rope control DBR管制法earliest start date 最早开工日economic order quantity 经济订购量economic part period 经济量期elimination,combination,rearrangement,simplification ECRS改善法emergency kanban 紧急看板employee empowerment 员工授权employee involvement 员工参与end user computing 使用者自建系统engineering to order 定单设计engineering change 设计变更engineering product structure 工程用产品结构表exception report 企业资料规划enterprise resource planning例外报告executive information system 主管信息系统existence test 存在测试expeditor 催料人员final assembly schedule 最终组装排程finished goods 完成品firm planned order 固定计划定单first in first out 先进先出fixed order quantity 定量批量法flow shop 流程生产工厂forecast 预测forecasthorizon预测期间fundamental data 基本资料gateway workstation 投料工作站graphic user interface 图形接口gross requirement 总需求group technology 群组技术hedge inventory 避险库存inbound queue control 输入端队列控制independent demand 独立需求input/output control 输入/输出控制intermittent production 间歇式生产inventory management 库存管理inventory status 库存状态inventory sub-type 库存副型态inventory type 库存型态item 材料(项目)item master 材料主档job 工件,工作job shop 工件生产工厂joint operation 联合作业just in time 及时供补kanban 看板kanban ceiling 看板界限latest start date 最晚开工日lead time 前置时间lead time offset 前置时间冲销least total cost 最低总成本批量法least unit cost 最低单位成本批量法level scheduling 平准化排程level production(linearity) 平准化生产linearity 定率生产load 负荷look ahead/look back 瞻前顾后法lot for lot 逐批批量法lot number 批号lot size 批量lot size inventory 批量库存lot sizing rule 批量法则low-level code 最低阶码maintenance, repair and operational supplies 间接物料make to order 定单生产make to stock 计划生产managerial product structure 管理用产品结构表manufacturing bom 制造单元manufacturing cell 制令单manufacturing order 制造规划与控制manufacturingplanning and control 制造资源规划master production scheduling 主生产排程master scheduler 主生产排程员material service sheduling 主服务排程material handling 材料搬运material requirement planning材料需求规划(计划) mean absolute deviation 平均绝对差modular bom 模块材料表modular production 模块化生产mps item MPS 项目mrp crusades MRP 改革运动mrp nervousness MRP不安定性multilevel mps 多阶主生产排程net change 净变法net requirement 净需求offset time 冲销时间one less at a time 一次减一点on-hand inventory 在库量on-order inventory 在途量open system platform 开放系统平台operations planning and control 作业规划与管制operations process chart 作业程序图option 选用件optional bom 选用材料件order interval 订购区间order point 订购点original equipment manuafacturer 原设备制造商outbound queue control 输出端队列控制overflow stockroom 溢量仓parent/component 父件/子件part number 件号part periodic balancing 量期平衡批量法past due 逾期量peg file 溯源文件pegging 溯源period length 期长periodic order quantity 定期批量法periodic review system 定期评估法phantom 幽灵材料phantom bom 幽灵材料表phantom component 幽灵子件picking order 领料单pipeline stock 管路库存plan-do-check-action cycle 计划-执行-检查-行动循环planned order receipts 计划定单收料planned order releases 计划定单发出planning bom 计划材料表planning horizon计划期间planning time fence 计划时栅point of use 使用点primary stockroom 基本仓priority control 优先次序控制priority planning优先次序规划preventive maintenance 预防性维护process flow chart 制程流程图process manufacturing 流程式生产product configuration system 产品构造系统product family 产品族product line 产品线product load profile 产品负荷表product structure 产品结构表product sub-line 产品副线production activity control 生产活动管制production rate 生产速率production plan 生产计划production planning生产规划production run 生产连project-based production 项目式生产projected available balance 预计可用量projected on-hand 预计在库量pseuo bom 假材料表pull signal 拉式讯号pull system 拉式系统purchase order 订购单purchase reuisition 请购单quantity-per 单位用量quick response 快速反应rated capacity 评估产量raw material 原材料reasonableness test 合理测试receiving order 收料单refill kanban 补充看板regeneration 再生法re-order point 再订购点法repetitive manufacturing 重复性生产replacement part 替代件replenishment plan 补充计划replenishment time 补充时间resource profile 资源负荷表resource requirementplanning资源需求规划rework kanban 重加工看板rolling kanban 滚动看板rolling schedule 滚动式排程rough-cut capacity planning粗略产量规划route 途程routing 途程表safety stock 安全存量safety time 安全时间safety order 销售定单scheduled receipts 在途量(已订未交量) scarp rate 报废率secondary stockroom 次级仓semi-finished goods 半成品serial number 序号setup 准备作业shop calendar 厂历shop floor 制造现场shop floor control 制造现场控制shop order 制令单significant numbering 显义编号standard coefficient 标准系数stock keeping unit 材料库存单位subsontract order 外包单super bom 超材料单supply chain management 供应链管理synchronized control 同步控制synchronized production 同步生产theoretical capacity 理论产量theory of constraints 限制理论three tier architecture 三层式架构throughtput 产出率time bucket 时段time phased order point 分期间订购点法transferring order 调拨单transportation inventory 运输库存total employee involvement 全员参与total preventive maintenance 全面预防性维护total productive maintenance 全面生产性维护total quality management 全面质量管理two bin system 双箱法two level mps 双阶主生产排程unit of measure 单位visual review system 目视评估法where-used report 用途表WIP inventory 在制品库存WIP tracking 在制品追踪work flow control system 工作流程控制系统work-in-process 在制品yield 良品率.。

生产运作管理chap

MRP provides time scheduling information specifying when each of the materials, parts, and components should be ordered or produced.
MRP in its basic form is a computer program determining how much of each item is needed and when it is needed to complete a specified number of units in a specific time period.
Day: 1 2 3 4 5 6 7 8 9 10
A Required
50
Order Placement
50
LT = 1 day
Next, we need to start scheduling the components that make up “A”. In the case of component “B” we need 4 B’s for each A. Since we need 50 A’s, that means 200 B’s. And again, we back the schedule up for the necessary 2 days of lead time.
Operations Management
Chapter 9
Material Requirements Planning
Outline
Global Company Profile Dependent Demand Dependent Inventory Model Requirements

最想要人工智能干什么的英语作文

最想要人工智能干什么的英语作文The rapid development of artificial intelligence (AI) technology has brought about significant changes in various aspects of our lives. From autonomous vehicles to virtual assistants, AI has the potential to revolutionize the way we live, work, and interact with the world around us. As such, many people are now pondering the question: what do we want AI to do for us?One of the most pressing issues facing humanity today is climate change. With rising global temperatures and extreme weather events becoming more frequent, the need for innovative solutions to mitigate the impact of climate change has never been more urgent. This is where AI could play a crucial role. By crunching vast amounts of data and analyzing complex patterns, AI systems could help us develop more efficient and sustainable ways of producing energy, managing resources, and reducing carbon emissions. For example, AI-powered smart grids could optimize energy distribution and consumption, while climate forecasting models could enable better preparedness for natural disasters.In the healthcare sector, AI is also poised to make a significant impact. From diagnosing diseases to designingpersonalized treatment plans, AI-powered systems have the potential to revolutionize the way we approach healthcare. By analyzing vast amounts of patient data and identifying patterns that human doctors might miss, AI could help us detect diseases earlier, develop more effective treatment strategies, and improve patient outcomes. In addition, AI-driven medical devices and robots could assist healthcare professionals in performing surgeries, administering medications, and providing patient care.In the field of education, AI could also play a transformative role. By personalizing learning experiences, AI-powered systems could help students of all ages and abilities reach their full potential. For example, adaptive learning platforms could tailor educational content to each student's individual needs and learning style, while intelligent tutoring systems could provide real-time feedback and support to help students master difficult concepts. Furthermore, AI could help educators identify areas where students are struggling and intervene before problems escalate, ultimately improving the overall quality of education.Beyond these specific applications, there are countless other ways in which AI could benefit society. From enhancing cybersecurity to improving transportation systems, AI has the potential to optimize efficiency, enhance productivity, and fosterinnovation across a wide range of industries. However, as we continue to unlock the power of AI, it is crucial that we consider the ethical implications of these technologies and ensure that they are used in ways that benefit all members of society.In conclusion, the possibilities of what AI could do for us are endless. By harnessing the power of AI to address pressing challenges and improve the quality of life for people around the world, we have the opportunity to create a more sustainable, equitable, and prosperous future for all. As we embark on this journey towards a more AI-enabled world, let us remember that the ultimate goal should be to use these technologies to enhance human well-being and promote the common good. Only by working together to harness the full potential of AI can we truly unlock its transformative power and create a better future for us all.。

2.HT第二课时(计划模式)

S表示每批订货成本 D为年总需求 (注：D与H的单位必须相同) 计算原理如右图：
质量创新发展和谐
宁波鸿腾精密制造股份有限公司
Ningbo City Hongteng Mechanical＆Electrical Co., Ltd
间断订货批量
许多制造作业的场合下，具体零部件需求的产生在间隔时间上趋向于没有规律性，且需求量也变化莫测，每次需求间隔期间，无需保持零部件存货处于储备状态，只要它在需要时可得到就行了。相关需求（dependent demand）的存货服务需要一种经调整过的方法来确定订货批量，这种批量称作“间断订货批量”（discrete lot sizing）
质量创新发展和谐
Байду номын сангаас
宁波鸿腾精密制造股份有限公司
Ningbo City Hongteng Mechanical＆Electrical Co., Ltd
基本原理
订货点法需考虑对提前期中需求的估计和安全库存，其中安全用来应付需求和提前期的波动。
假设使用率是固定的，库存将沿着斜线下降，达到订货点时发出一补货订单，订货量为EOQ。在提前期中，库存继续下降，到提前期末，收到了补充订货；于是库存增加了EOQ，库存的升降循环又重新开始。另一假设是：补货是按时完成的。
EOQ概念适用于下列情况： 1）物品成批地，通过采购或制造得到补充，而非连续地被获得。 2）销售或使用的速率是均匀的，而且低于该物品的正常获得速率，因而可产生显著数量的库存。 EOQ概念不适用于为库存而生产的一切物品。在下列情况下ＥＯＱ概念是没有价值的。 1）客户规定了数量。 2）生产运行批量受设备能力限制。 3）产品只能短期储存的。 4）工具寿命限制了运行时间。 5）原料的批量限定了订货量。

生产计划与排程(英文)

Rhythm FP Architecture (cont.)
Production Planner
Rhythm FP Client
Material Planner
Rhythm FP Client
Report Reader
Rhythm FP Client
Rhythm FP Server
Production Server
• Manufactured parts (finish goods and intermediate parts) are supplied by WIP or manufacturing orders (work orders).
Process Flow in FP
• Identify Material and
??路漫漫其悠远rhythmfpfeatures??路漫漫其悠远listofsomefeatures?transferbatching?manufacturinglotsizingcmo?procurementlotsizing?wipreporting?advancedscheduleras?duedatequoting?batchclient?alternatesalternatepartsalternateresourcesalternaterouting?ecneffectivitydateeffectivityuseupeffectivity?sequencedependentsetups?resourcemodeling??路漫漫其悠远usingrhythmfp??路漫漫其悠远usingfpbasicuientities?problemwindows?reports?demandorders?demandorderplan?mfgorders?mfgorderplan?partbuffer??路漫漫其悠远usingfpproblemwindows?fphastheoutstandingproblemanalyticalcapabilitiesshortorderproblemlateorderproblemcapacityshortageproblemmaterialshortageproblemlatemanufacturingpartsproblem??路漫漫其悠远usingfpproblemwindowscont

A Supplier’s Optimal Quantity Discount

A Supplier’s Optimal Quantity Discount Policy Under Asymmetric InformationCharles J.Corbett•Xavier de GrooteThe Anderson School at UCLA,110Westwood Plaza,Box951481,Los Angeles,California90095-1481INSEAD,Fontainebleau,Francecharles.corbett@I n the supply-chain literature,an increasing body of work studies how suppliers can useincentive schemes such as quantity discounts to inﬂuence buyers’ordering behaviour,thus reducing the supplier’s(and the total supply chain’s)costs.Various functional forms for such incentive schemes have been proposed,but a critical assumption always made is that the supplier has full information about the buyer’s cost structure.We derive the optimal quantity discount policy under asymmetric information and compare it to the situation where the supplier has full information.(Supply Contracts;Coordination;Lot Sizing;Quantity Discounts;Asymmetric Information)1.IntroductionThe most well-established framework for studying coordination in supply chains is perhaps that of choos-ing lot sizes in a tightly coupled system with lot-for-lot production.Imagine a single supplier with high setup costs shipping to a single buyer with low setup costs, where the supplier’s production lot size is equal to the lot size shipped to the buyer.Clearly,letting either party determine lot size independently would lead to inefﬁcient outcomes.Starting with(among others) Goyal(1976),Monahan(1984),and Lee and Rosenblatt (1986),the joint economic lot-sizing literature has examined the case where the supplier wishes to in-duce the buyer to choose a higher lot size than she would of her own accord.Reviews by Goyal and Gupta(1989)and Weng(1995)show how coordination can be achieved in integrated lot-sizing models with deterministic demand.Their work and that of others provides valuable insights into how and when quan-tity discount schemes can be used to achieve jointly optimal outcomes.Weng(1995)shows that,as long as the quantity discount offered does not affect demand, a quantity discount scheme can indeed yield jointly optimal lot sizes.Several different types of quantity discount schemes have been proposed in the litera-ture,including all-units and incremental discounts, and with a variety of functional forms.Appropriately designed,any of these schemes can lead the buyer to choose the jointly optimal lot size and make both parties better off than without any form of coordina-tion.A critical assumption made throughout this litera-ture,though,is that the supplier has full information, and can design the quantity discount scheme accord-ingly;this rarely will be true in practice.In this paper we drop that full information assumption and derive the supplier’s optimal quantity discount scheme when the buyer holds private information about her cost structure.Speciﬁcally,imagine that a supplier and buyer are about to start doing business together,and that the supplier’s setup costs are(considerably)larger than the buyer’s.We examine the lot-for-lot produc-tion system that is traditional in this context,so the supplier’s production lot size equals the transfer lot size.The simplest mode of operating would be to agree on a price and let the buyer order as frequently as she wishes;clearly this will lead to a much higher order frequency than the supplier would like.TheManagement Science©2000INFORMS0025-1909/00/4603/0444$05.00supplier wants to induce the buyer to order less frequently,realizing she will be reluctant to do so,but, as he cannot observe the buyer’s cost structure,he does not know how“reluctant”she truly is.The supplier needs to design an incentive mechanism to overcome this.The buyer’s reluctance can stem from several causes and is often not easily quantiﬁable even to the buyer;from the supplier’s standpoint,the buyer’s“holding costs”are anything but known.(One could perform the analyses in this paper with infor-mation asymmetry about the buyer’s setup costs,but, as these are generally assumed to be far less than the supplier’s,this case is less interesting.)In this paper we compare two contracts:the suppli-er’s optimal contract under full information(case FI) and under asymmetric information(case AI).The asymmetric information case can also be interpreted as the optimal contract to offer to a group of hetero-geneous buyers when the supplier cannot l and Staelin(1984)were unable to obtain a closed-form solution to a closely related problem:Given N groups of buyers of different sizes, with holding costs,order costs,and demand rates varying between groups but not within groups,what is the optimal pricing policy?Our analysis is a partial solution to their problem:we only vary the buyer’s holding costs,but derive the optimal quantity dis-count policy for an arbitrary continuum of buyer types.In§2,we develop the basic model under full information.Section3derives the optimal contract under asymmetric information.We compare the per-formance of both schemes in§4;conclusions and further research are discussed in§5.2.The Basic Model:FullInformation in a Lot-for-LotSystemLet ks and kbdenote the setup costs incurred by thesupplier and buyer respectively and let hbdenote the buyer’s unit holding cost per unit time.(All notation issummarized in Table1.)We assume ks and kbareconstant and common knowledge.Two decisions are considered:order lot size,Q,and the contract speci-fying the quantity discount P(Q)per unit time fromsupplier to buyer.The buyer’s operating cost is Cb (hb,Q)ϭ(kbd/Q)ϩ(hb/2)Q;Qb(hb)ϭ͌b b is thelot size that minimizes Cb(hb,Q).We assume that supplier and buyer work under a lot-for-lot system,asis common in literature on integrated buyer-supplierlot sizing.(This assumption is relaxed in Corbett andde Groote1997).Under lot-for-lot,the supplier’s costfunction is Cs(Q)ϭksd/Q so that his individualoptimum Qsis the largest allowable lot size.Thesystem(or joint)operating cost is Cj(hb,Q)ϭCb(hb,Q)ϩCs(Q)ϭ[(kbϩks)d/Q]ϩ12hbQ and thecorresponding jointly optimal lot size is Qj(hb)ϭ͌2(kbϩks)d/hb.The assumption that the supplierhas the larger setup cost,i.e.ksՆkb,is the starting point for the joint economic lot sizing literature.Assuming that trade takes place,the supplier’s andbuyer’s total or net costs are their operating costs netof the discount P(Q),so that TCs(Q):ϭCs(Q)ϩP(Q)and TCb(hb,Q):ϭCb(hb,Q)ϪP(Q).If no trade takes place,their total costs are equal to their reservation netcost levels TCsϩand TCbϩ,respectively:They will choose not to trade with each other if the net costs(after discount)of doing so would exceed these reser-vation values.These could be the costs of the marketTable1NotationNotationiϭFI,AI cases consideredFI full informationAI asymmetric information with revelationkb,ksbuyer’s and supplier’s setup costshbbuyer’s unit holding costs per period[hb,h៮b]range of buyer holding cost h bF(hb),f(hb)supplier’s prior distribution and density over hb E[X]expectation of a random variable Xd demand per periodCb,Cs,Cjbuyer,supplier,and joint cost function(excluding discount)Q lot sizeQb,Qs,Qjbuyer’s,supplier’s,and jointly optimal lot size(without contracting)Pi(hb)payment from supplier to buyer,as a functionof hb,under contract iP˙(hb)ϭDh bP(hb)partial derivative of P(hb)with respect to hb TCbϩ,TCsϩmaximum(net)cost level acceptable to buyerand supplierTCb,i,TCs,i,TCj,ibuyer,supplier,and joint net cost function incase i,after discountalternatives or,if there are none,TC b ϩcould be the cost of the market opportunity the buyer forgoes by not trading with the supplier,and TC s ϩcould be the cost to the supplier of converting the process to supply a different market altogether.If their joint costs underQ j exceed TC s ϩϩTC b ϩ,they will de ﬁnitely not trade;this gives the condition that ͌2d (k b ϩk s )h b ՅTC s ϩϩTC b ϩ.Only the buyer knows h b .This condition canbe rewritten to give an upper bound h៮b where h b Յh៮b Յ͑TC s ϩϩTC b ϩ͒22d ͑k b ϩk s ͒.(1)If (1)is met under full information ,mutually bene ﬁcial trade can always take place.Under asymmetric infor-mation,however,(1)is necessary but not suf ﬁcient.Below,we let the supplier choose a cut-off point h *b such that he will choose not to trade with buyers with h b Ͼh *b ,as the supplier ’s total costs would then exceed TC s ϩ.Assume that the supplier holds a prior distribu-tion F (h b )over h b with support [h b ,h៮b ].Demand per unit time d is known and constant.In particular it is not affected by the lot sizing or contracting decisions.The supplier has the initiative to propose contracts but the buyer may refuse them.This corresponds to a principal-agent framework with supplier as principal and buyer as agent with “adverse selection ”(ex ante information asymmetry)but no “moral hazard ”(un-observability of effort);see Laffont and Tirole (1993)for more on these concepts.The need to coordinate stems from the fact that the buyer,in optimizing her own costs,does not consider the cost impact for the supplier,leading to Q b ϽQ j ϽQ s .The supplier can in ﬂuence the buyer ’s choice of Q by speci ﬁcation of the contract P (Q ),leading the buyer to minimize her net costs TC b (h b ,Q )ϭC b (h b ,Q )ϪP (Q ).By doing so,he can induce the buyer to choose a Q that reduces his operating cost C s (Q )and the total system cost TC j (h b ,Q ).However,this is done at the expense of the discount P (Q ).In designing a contract,the supplier must therefore trade off the efﬁciency of the outcome against the sharing of the resulting ef ﬁciency gains between the two parties.It is easy for the supplier to induce ef ﬁciency by passing on his setup cost,charging the buyer k s for each delivery,thus making the buyer internalize all variable costs(the “individually rational and responsible decision ”(IRRD)approach suggested by Joglekar and Tharthare 1990).The buyer will then choose the jointly optimal lot size Q j .To make this contract palatable to the buyer (regardless of h b )the supplier must offer a high discount;this approach is therefore ef ﬁcient but the resulting cost sharing is unattractive for the supplier.In the principal-agent framework,the supplier ﬁrst proposes a “menu of contracts ”or discount scheme,specifying the discount P (q )offered for any lot size q .The buyer decides whether or not to accept the con-tract and,if she accepts,chooses some order lot size Q .The supplier gives the buyer a discount of P (Q ).The supplier ’s problem can be formalized as follows:᏿min Q ,P ͑Q ͒E ͓TC s ͑Q ͔͒ϭE ͓P ͑Q ͒ϩC s ͑Q ͔͒(2)subject toIC:Q ϭarg min q͕TC b ͑h b ,q ͖͒ϭarg min q͕C b ͑h b ,q ͒ϪP ͑q ͖͒᭙h b ʦ͓h b ,h៮b ͔(3)IRb:TC b ͑h b ,Q ͒ϭC b ͑h b ,Q ͒ϪP ͑Q ͒ՅTC bϩ᭙h b ʦ͓h b ,h៮b ͔(4)IRs:TC s ͑Q ͒ϭC s ͑Q ͒ϩP ͑Q ͒ՅTC s ϩ.(5)The expectation in (2)will be de ﬁned more preciselybelow.The supplier minimizes his expected total cost taking into account the buyer ’s reaction to the contract as expressed in the two constraints.Equation (3)is the incentive-compatibility (IC)constraint,and accounts for the buyer ’s selection of a lot size that minimizes her net costs including the discount.Equation (4)is the buyer ’s individual-rationality (IRb)constraint,and en-sures the buyer ’s participation:The net costs incurred by the buyer (again after discount)must be at mostequal to the reservation costs TC b ϩ.If we were to omit IRb,the supplier could set P (Q )arbitrarily negative.No buyer would ever wish to contract with such a supplier,hence the need to restrict the supplier ’s behavior by including IRb.Equation (5)is the equiv-alent condition for the supplier,and ensures that hewill not end up worse off from the contracting process than from the outside alternative.The range over which IRs must hold is de ﬁned below.In this case h b is observed by the supplier.The optimal contract is equivalent to the selection of the joint economic lot size Q j (h b ),as in Goyal (1976),Banerjee (1986),and others,leaving all ef ﬁciency gains to the supplier.This outcome can be implemented with the contractP FI ͑h b ,Q ͒ϭΆC b ͑h b ,Q j ͑h b ͒͒ϪTC bϩif Q ՆQ j ͑h b ͒ϭͱ2͑k s ϩk b ͒dh bϪTC bϩotherwise.(6)The buyer will choose Q j (h b ),resulting in net costsTC b ,FI ͑h b ͒ϭTC bϩ(7)TC s ,FI ͑h b ͒ϭC j ͑h b ,Q j ͑h b ͒͒ϪTC bϩϭͱ2͑k s ϩk b ͒dh b ϪTC b ϩ.(8)This is known as the ﬁrst-best solution.Condition (1)guarantees that TC s ,FI (h b )ՅTC s ϩfor all h b ʦ[h b ,h៮b ].Below we evaluate contracts when the supplier has noinformation about h b other than the range [h b ,h៮b ]and a prior distribution F (h b ).3.Optimal Contract Under Asymmetric Information (Case AI)The approach we take here relies on the revelation principle (explained below)and closely follows Laf-font and Tirole (1993)and Corbett (1997).We refor-mulate the problem as a direct revelation game and use optimal control theory to derive the supplier ’s optimal contract.A common assumption needed here is the following:Assumption 1.Decreasing reverse hazard rate :D h b [f (h b )/F (h b )]Յ0.Many common distributions satisfy Assumption 1,including uniform,normal,logistic,chi-squared,andexponential;see Shaked and Shanthikumar (1994,p.24)for more on the reverse hazard rate.If f (x )de ﬁned on any (possibly in ﬁnite)interval [l ,u ]satis ﬁes As-sumption 1,its truncation f T (x )to the interval [h b ,h៮b ],de ﬁned by f T (x ):ϭf (x )/(F (h៮b )ϪF (h b ))does too.See Bagnoli and Bergstrom (1989)for an extensive discus-sion of the related condition of log-concavity and distributions which meet these conditions.Distribu-tions with thin tails cause problems;to rule these out,we use an additional assumption to guarantee incentive-compatibility:Assumption 2.[F (h b )/h b f (h b )]Յ(k s /k b )for all h b .Neither of these assumptions (1and 2)on F (h b )are in fact necessary conditions.Instead of proposing a single contract P (Q )as in the full-information case,the supplier now offers a menu of contracts {Q ,P (Q )},letting the buyer choose a speci ﬁc (Q ,P (Q ))-pair from the menu.We parame-terize Q and P on h b ;offering a {Q (h b ),P (h b )}menu is equivalent to a {Q ,P (Q )}menu,though that equiva-lence relation need not exist in closed form.Whether or not contracting is explicitly based on h b ,the sup-plier can always infer h b after the fact from the buyer ’s selection of (Q ,P (Q )).The contracting procedure is then as follows.At the outset,the buyer knows h b ,unobserved by the supplier.The supplier offers amenu {Q (ˆh b ),P (ˆh b )},linking lot size Q (ˆh b )to the discount P (ˆh b )for any ˆh b ʦ[h b ,h ៮b ]the buyer an-nounces.The buyer chooses lot size Q (ˆh b )and dis-count P (ˆh b ),effectively announcing ˆh b .After contract-ing,lot size is ﬁxed (forever)at Q (ˆh b),and the buyer receives a per-period discount of P (ˆh b).Buyer and supplier incur net costs per period of C b (h b ,Q (ˆh b))ϪP (ˆh b )and C s (Q (ˆh b ))ϩP (ˆh b ),ter we see that,under the supplier ’s optimal menu of con-tracts,if constraint IRb is satis ﬁed for some h *b ,it will also be satis ﬁed for all h b Յh *b .For large values of h b ,though,constraint IRs may not hold.To ensure that both players ’individual-rationality constraints are sat-is ﬁed,the supplier can set h *b such that IRb and IRs are met for all h b Յh *b and such that both parties will revert to their outside alternatives whenever h b Ͼh *b .This is explained more formally below.The revelation principle (Laffont and Tirole 1993,p.120)states that if there is an optimal contract for the supplier,then there exists an optimal contract under which the buyer will truthfully reveal her holding cost.This allows us to restrict our attention to such revelation mechanisms.Intuitively,this is easy to see:the supplier can predict exactly how a buyer withholding cost h b would behave,and therefore what ˆh bshe would announce when faced with any given quantity discount scheme.Therefore,he can constructa mapping ˆhb (h b)and use this in designing the dis-count scheme {Q (ˆh b ),P (ˆh b)}.The revelation principle allows us to formulate an incentive-compatibility con-straint on P AI (h b ),which requires that it is optimal fora buyer with holding cost hb to indeed reveal ˆh b ϭh b.Write the derivative D h b P (h b )of P (h b )with respect toh b as P˙(h b ).Presented with a contract {Q AI (h b ),P AI (h b )},the buyer chooses which hˆb to reveal by solvingᏮAI minhˆb ͭk b d Q AI ͑h ˆb ͒Ϫh b2Q AI ͑hˆb ͒ϪP AI ͑h ˆb ͒ͮ.(9)Taking the ﬁrst-order condition with respect to ˆh band requiring it be satis ﬁed at ˆh b ϭh b yields the incentive-compatibility constraint:P˙AI ͑h b ͒ϭͩh b 2Ϫk b dQ AI ͑h b ͒2ͪQ˙AI ͑h b ͒.(10)The common formulation of the buyer ’s individual-rationality constraint IRb requires that the contract isacceptable to any buyer,regardless of h b :TC bϩՆTC b ͑h b ,Q ͑h b ͒͒ϭC b ͑Q ͑h b ͒͒ϪP ͑h b ͒᭙h b ʦ͓h b ,h៮b ͔.(11)We relax this requirement,and give the supplier theoption to refuse to trade with buyers with h b Ͼh *b .By default,IRb is then met for any h b Ͼh *b ,and for h b Յh *b IRb becomesTC b ϩՆTC b ͑h b ,Q ͑h b ͒͒ϭC b ͑Q ͑h b ͒͒ϪP ͑h b ͒᭙h b ʦ͓h b ,h *b ͔.(12)Of course,if h *b Նh ៮,the two are equivalent.To ﬁnd theoptimal menu of contracts,the supplier solves theoptimal control problem:᏿AImin Q ͑h b ͒,P ͑h b ͒,h *b ʦ͓h b ,h ៮b ͔E h b ͓TC s ͑h b ͔͒ϭE h b Յh *b ͫk s dQ ͑h b ͒ϩP ͑h b ͒ͬϩE h b Ͼh *b ͓TC *s ͔(13)subject to the incentive-compatibility and individual-rationality constraints.More details on how to solve thisproblem are provided in Corbett and de Groote (1997).Proposition 1.In the optimal discount scheme under asymmetric information ,the supplier will only trade withbuyers with h b Յh *b where h *b is the solution toTC s ϩϭk s dQ AI ͑h *b ͒ϩP AI ͑h *b ͒,(14)and h *b is increasing in TC s ϩ.For h b ʦ[h b ,h *b ],the lot sizeand discount scheme are given byQ AI ͑h b ͒ϭͱ2͑k s ϩk b ͒dh b ϩF ͑h b ͒f ͑h b ͒(15)P˙AI ͑h b ͒ϭͩh b 2Ϫk b dQ AI ͑h b ͒2ͪQ˙AI ͑h b ͒.(16)For h b ʦ]h *b ,h ៮b ],no trade takes place and both playersrevert to their outside alternatives ,incurring costs TC s ϩand TC b ϩ,respectively .When the prior F (h b )is uniform ,the optimal policy and corresponding cost levels for h b ʦ[h b ,h *b ]are :Q AI ͑h b ͒ϭͱ2͑k s ϩk b ͒d2h b Ϫh b(17)P AI ͑Q ͒ϭh b Q 4Ϫ͑k s Ϫk b ͒d 2Qϩ12ͱ2͑k s ϩk b ͒͑2h *b Ϫh b ͒d ϪTC bϩ(18)TC b ,AI ͑h b ͒ϭTC b ϩϪ12ͱ2͑k s ϩk b ͒d ͑ͱ2h *b Ϫh bϪͱ2h b Ϫh b ͒(19)TC s ,AI ͑h b ͒ϭ12ͱ͑k s ϩk b ͒d ͑ͱ2h *b Ϫh b ϩͱ2h b Ϫh b ͒Ϫ12͑h b Ϫh b ͒Q AI ͑h b ͒ϪTC bϩ(20)TC j ,AI ͑h b ͒ϭͱ2͑k s ϩk b ͒d ͑2h b Ϫh b ͒Ϫ12͑h b Ϫh b ͒Q AI ͑h b ͒(21)There is a probability 1ϪF (h *b )of no trade takingplace.For h b Յh *b ,Q AI (h b )is decreasing in h b .In general,h *b cannot be found explicitly as it is impossi-ble to write P AI (h b )explicitly;more detailed analysisof h *b is left for further research.For TC s ϩsuf ﬁcientlylarge,h *b ϭh ៮b ,which is the case (implicitly)tradition-ally assumed in the economics literature.Whether it can be written in closed form or not,P AI (h b )can always be interpreted as a quantity discount.Both Q AI (h b )and P AI (h b )are strictly decreasing in h b ,so there is a one-to-one mapping P AI (Q )which itself is increasing:A larger order quantity Q will lead to a larger lump-sum discount P AI (Q )(and hence a larger per-unit discount P AI (Q )/d ).For the uniform case,the quantity discount can be written explicitly as in (18).Here,the discount scheme P AI (Q )is increasing and concave in Q .It is interesting to note that the variable part of the discount depends on the “best practice ”value h b ,while theconstant part also depends on the “worst-case ”value h *b.Furthermore,supplier ’s and buyer ’s costs are decreasing in h b ;the buyer ’s net costs are (by construction)equal to TC b ϩat h *b.At h b ,the resulting lot size Q AI (h b )is equal to the jointly optimal lot size;as h b increases,the discrepancy between Q FI (h b )and Q AI (h b )increases,their ratio be-ing Q AI (h b )/Q FI (h b )ϭ͌b b ϩ[F (h b b The expressions derived in Proposition 1may super ﬁcially resemble those in Min (1992);that paper however addresses the very different pricing problem where total demand depends on the price charged by the supplier,but where there is no lot-size-related coordi-nation issue of any kind.paring the ContractsHaving derived the optimal contracts under full and under asymmetric information,the comparisons be-tween them are now immediate.In the full informa-tion case,when h b ϭh៮b ,we have Q AI ϭQ FI ϭQ j and TC s ,AI ϭTC s ,FI and TC b ,AI ϭTC b ,FI ϭTC b ϩ.Let TC j ,b and TC j ,j represent total joint costs without coordina-tion and under the joint optimum respectively.Proposition 2.Under asymmetric information ,the lot sizes satisfy :Q b ͑h b ͒ՅQ AI ͑h b ͒ՅQ FI ͑h b ͒ϭQ j ͑h b ͒᭙h b ʦ͓h b ,h៮b ͔.(22)Global ef ﬁciency is reduced by information asymmetry butcontracting is more ef ﬁcient than no form of coordination :TC j ,b ՆTC j ,AI ՆTC j ,FI ϭTC j ,j᭙h b ʦ͓h b ,h៮b ͔.(23)The supplier ’s expected net costs increase under informa-tion asymmetry :TC s ϩՆE h b ͓TC s ,AI ͔ՆE h b ͓TC s ,FI ͔.(24)The buyer ’s net costs decrease when she has private infor-mation :TC bϩϭTC b ,FI ͑h b ,Q FI ͑h b ͒͒ՆTC b ,AI ͑h b ,Q AI ͑h b ͒͒᭙h b ʦ͓h b ,h៮b ͔.(25)The ﬁrst inequality in (22)follows from Assumption2;the second follows directly from the de ﬁnition of Q AI and Q FI .One cannot meaningfully compare TC b ,b with TC b ,FI and TC b ,AI or TC s ,b with TC s ,FI and TC s ,AI as this would require additional assumptions about pay-ment ﬂows before contracting;these assumptions would then drive the resulting comparisons.5.Conclusions and Future ResearchThis paper has derived the optimal quantity discount scheme for the joint economic lot-sizing problem un-der asymmetric information.Strictly speaking,to im-plement a quantity discount scheme as a reduction of unit price,one would have to look into the impact on the holding cost structure.One could also combine this with letting total demand vary with the discount offered,as in Weng (1995).Such extensions should not materially alter the key qualitative insights of the current work.One can easily redo this analysis with uncertainty about the buyer ’s setup cost;this leads to similar qualitative results.Allowing uncertainty on several dimensions independently,e.g.following Lal and Staelin ’s (1984)model of heterogeneous buyer groups,does not seem manageable with current tech-niques.Finally,we need to further explore how else one might model the presence of outside alternatives rather than through the individual-rationality con-straint commonly used in the economics literature,either using the relaxation proposed here or in some other way.11This paper is partly based on discussions with Xavier de Groote before his death in1996.Financial support from INSEAD and from the Owen Graduate School of Management at Vanderbilt University is gratefully acknowledged,as are comments from Marty Larivie`re, the referees and the associate editor.ReferencesBagnoli,M.,T.Bergstrom.1989.Log-concave probability and its applications.Working paper,ewp-mic/9410002,University of Michigan,Ann Arbor,MI Available at͗/eprints/mic/papers/9410/941002.wps͘Banerjee,A.1986.A joint economic-lot size model for purchaser and vendor.Decision Sci.17292–311.Corbett,C.J.1997.Cycle stocks,safety stocks,and consignment stocks.Oper.Res.Under revision.,X.de Groote.1997.Integrated supply-chain lot sizing under asym-metric information.Working paper,The Anderson School at UCLA, Los Angeles,CA.Presented at the First Xavier de Groote Memorial Conference,INSEAD,Fontainebleau,France,May16,1997.Goyal,S.K.1976.An integrated inventory model for a single sup-plier—single customer problem.Internat.J.Prod.Res.15107–111.,Y.P.Gupta.1989.Integrated inventory models:the buyer-vendor coordination.European J.Oper.Res.41261–269. Joglekar,P.,S.Tharthare.1990.The individually responsible and rational decision approach to economic lot sizes for one vendor and many purchasers.Decision Sci.21492–506.Laffont,J.-J.,J.Tirole.1993.A Theory of Incentives in Procurement and Regulation.MIT Press,Cambridge,MA.Lal,R.,R.Staelin.1984.An approach for developing an optimal discount pricing policy.Management Sci.301524–1539. Lee,H.L.,M.J.Rosenblatt.1986.A generalized quantity discount pricing model to increase supplier’s proﬁt.Management Sci.32 1177–1185.Min,K.J.1992.Inventory and quantity discount pricing policies under proﬁt maximization.Oper.Res.Letters11187–193. Monahan,J.P.1984.A quantity discount pricing model to increase vendor proﬁts.Management Sci.30720–726.Shaked,M.,J.G.Shanthikumar.1994.Stochastic Orders and Their Applications.Academic Press,Inc.,San Diego,CA.Weng,Z.K.1995.Channel coordination and quantity discounts.Management Sci.411509–1522.Accepted by Christopher S.Tang;received September15,1997.This paper was with the authors9months for2revisions.。

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Abstract: One of the basic assumptions of the classical dynamic lot-sizing model is that the aggregate demand of a given period must be satisfied in that period. Under this assumption, if backlogging is not allowed then the demand of a given period cannot be delivered earlier or later than the period. If backlogging is allowed, the demand of a given period cannot be delivered earlier than the period, but can be delivered later at the expense of a backordering cost. Like most mathematical models, the classical dynamic lot-sizing model is a simplified paraphrase of what might actually happen in real life. In most real life applications, the customer offers a grace period - we call it a demand time window - during which a particular demand can be satisfied with no penalty. That is, in association with each demand, the customer specifies an earliest and a latest delivery time. The time interval characterized by the earliest and latest delivery dates of a demand represents the corresponding time window. This paper studies the dynamic lot-sizing problem with demand time windows and provides polynomial time algorithms for computing its solution. If shortages are not allowed, the complexity of the proposed algorithm is O(T2). When backlogging is allowed, the complexity of the proposed algorithm is O(T3).
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earliest and a latest delivery time, denoted by Ei and Li, respectively, where Ei ≤ Li. Hence, the interval [Ei, Li] represents the time window corresponding to di. As the title suggests, this paper studies the dynamic lot-sizing problem with demand time windows and provides polynomial time algorithms for computing its solution. The following two cases are considered: • • Shortages are not allowed so that each di must be delivered during its corresponding time window, i.e., no earlier than Ei and no later than Li. Backlogging is allowed, i.e., demand di cannot be delivered earlier than Ei, but can bnse of backordering costs. The dynamic lot-sizing problem with demand time windows has important applications in third party warehousing and vendor managed inventory practices. A detailed discussion of practical motivations and a brief review of relevant literature are presented in Section 2. The notation is introduced and some structural properties of the problem are proved in Section 3. Sections 4 and 5 focus on developing polynomial time algorithms for computing the optimal solution under different backordering assumptions. If shortages are not allowed, the complexity of the proposed algorithm is O(T2), and if backlogging is allowed, the complexity of the proposed algorithm is O(T3). Section 5 presents an interesting application in location planning which is related to the dynamic lot-sizing model with demand time windows. Finally, a brief summary and our concluding remarks are furnished in Section 6. 2. Problem Motivations and Related Literature The dynamic lot-sizing problem has received a significant amount of academic attention since it was first introduced more than four decades ago. The solution technique, known as the Wagner-Whitin algorithm, has long been regarded as one of the basic methods in production planning and inventory control. For a brief summary of results and the history of this model, see the textbooks by Bramel and Simchi-Levi (1997), Johnson and Montgomery (1974), and Silver et al., (1996). In the early years, due
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1. Problem Context and Definition The classical dynamic lot-sizing problem considers a facility, possibly a warehouse or a retailer, which faces dynamic demand for a single item over a finite horizon (Wagner and Whitin 1958). The facility places orders for the item from a supply agency, e.g., a manufacturer or a supplier, which is assumed to have an unlimited quantity of the product. The model assumes a fixed ordering (setup) cost, a linear procurement cost for each unit purchased, and a linear holding cost for each unit held in inventory per unit time. Shortages at the warehouse/retailer may or may not be allowed, and depending on how shortages are modeled, a linear stockout cost may accrue for every unit backordered per unit time. Given the time varying demand and cost parameters, the problem is to decide when and how much to order at the facility in each period so that all demand is satisfied at minimum cost. The basic assumption of the classical dynamic lot-sizing model is that the time varying demand is known in advance. Let T denote the length of the planning horizon over which the demands, denoted by di, i ∈ {1,…,T}, should be satisfied. Under the assumptions of the classical model, di represents the aggregate demand (placed by all customers) that must be satisfied in period i ∈ {1,…,T}. If backlogging is not allowed then di cannot be delivered earlier or later than i ∈ {1,…,T}. If backlogging is allowed, di cannot be delivered earlier than i, but it can be delivered later at the expense of backordering costs. Like most mathematical models, the classical dynamic lot-sizing model is a simplified paraphrase of what might actually happen in real life. The assumption that the values of di, i ∈ {1,…,T}, are known in advance is applicable if supply contracts are signed ahead of time designating deliveries for the next few periods (Bramel and Simchi-Levi 1997, p. 165). However, under a typical supply contract, the customer offers a grace period - we call it a demand time window - during which a particular demand can be satisfied with no penalty. That is, associated with each di, the customer specifies an