Optimization of Downtilts Adjustment Combining
预算调整原则 英文版
预算调整原则英文版The principle of budget adjustment refers to the guidelines and criteria used to make changes to a budget. There are several key principles that are commonly applied when adjusting a budget:1. Flexibility: Budgets should be flexible enough to accommodate changes in circumstances, such as unexpected expenses or revenue shortfalls. This may involve setting aside contingency funds or having the ability to reallocate funds from one area to another as needed.2. Prioritization: When adjusting a budget, it is important to prioritize spending based on the most critical needs of the organization or project. This may involve identifying essential expenses and ensuring that they are adequately funded before allocating resources to less critical areas.3. Transparency: The process of budget adjustmentshould be transparent, with clear documentation of the reasons for the changes and the impact on the overall budget. This helps to ensure accountability and allows stakeholders to understand the rationale behind the adjustments.4. Monitoring and Evaluation: Budget adjustments should be accompanied by ongoing monitoring and evaluation to assess their impact and effectiveness. This may involve setting performance indicators and regularly reviewing the budget to ensure that it remains aligned with the organization's goals and objectives.5. Stakeholder Involvement: It is important to involve relevant stakeholders in the process of budget adjustment, particularly those who will be directly affected by the changes. This may include seeking input from department heads, finance teams, and other key personnel to ensurethat adjustments are well-informed and have buy-in from those impacted.6. Long-term Planning: Budget adjustments should bemade with an eye towards long-term financial planning and sustainability. This may involve considering the potential implications of adjustments on future budgets and ensuring that any changes are aligned with the organization's long-term financial goals.These principles provide a framework for making responsible and strategic adjustments to a budget, ensuring that changes are made in a thoughtful and well-informed manner. By adhering to these principles, organizations can maintain financial stability and effectively manage their resources in the face of changing circumstances.。
资本结构静态优化方法
资本结构静态优化方法Optimizing capital structure is a crucial task for businesses aiming to maximize their value and minimize risk. 资本结构优化是企业最大化价值和最小化风险的一个关键任务。
It involves deciding on the mix of debt and equity that the company should use to finance its operations. 这涉及决定公司应该使用多少债务和股本来资助其运营。
There are various static optimization methods that can help companies determine the most efficient capital structure. 有各种静态优化方法可以帮助公司确定最有效的资本结构。
These methods include the Net Income Approach, Net Operating Income Approach, Traditional Approach, and Modigliani-Miller Theorem. 这些方法包括净收入法、净营业收入法、传统法以及莫迪格利安尼-米勒定理。
The Net Income Approach focuses on maximizing the wealth of shareholders by increasing the proportion of debt in the capital structure. 净收入法侧重于通过增加资本结构中的债务比例来最大化股东的财富。
By utilizing debt, the company can benefit from the tax shield that comes with the interest payments. 通过利用债务, 公司可以从随利息支付而来的税收盾牌中受益。
Optimization of InjectionWithdrawal Schedules for Natural Gas Storage Facilities
Optimization ofInjection/Withdrawal Schedules for Natural Gas Storage Facilities∗Alan HollandCork Constraint Computation Centre,Department of Computer Science,University College Cork,Cork,IrelandAbstractControl decisions for gas storage facilities are made in the face of ex-treme uncertainty over future natural gas prices on world markets.Weexamine the problem faced by owners of storage contracts of how to man-age the injection/withdrawal schedule of gas,given past price behaviorand a predictive model of future prices.Real options theory providesa framework for making such decisions.We describe the theory behindour model and a software application that seeks to optimize the expectedvalue of the storage facility,given capacity and deliverability constraints,via Monte-Carlo simulation.Our approach also allows us to determine anupper bound on the expected valuation of the remaining storage facilitycontract and the gas stored therein.The software application has beensuccessfully deployed in the energy trading division of a gas utility.1IntroductionThis work focuses on gas storage facilities that consist of partially depleted gas fields such as the Roughfield in the Southern North Sea,18miles from the east coast of Yorkshire.There is a shortage of gas storage facilities worldwide that has contributed to an increase in their value[9].There is,therefore,a greater incentive for optimizing its utilization given the rising costs in storage contracts. Many of these storage facilities were originally developed to produce natural gas.Fields can be converted to storage facilities,enabling gas to be stored within the reservoir,thousands of feet underground or under the seabed and withdrawn to meet peaks in demand.These facilities do not supply gas directly to domestic and industrial end users.Instead,they act as a storage facility for gas shippers and suppliers,allowing gas to be fed into a transmission system at times of peak demand(e.g.winter)or withdrawn from the grid and re-injected into the reservoir at times of low demand(e.g.summer).The movement of gas either into or out of the reservoir is based on“nominations”made by gas shippers as a result of demands placed on them by their end customers.These facilities have various deliverability rates depending on their size and physical ∗The author is very grateful to the energy trading team at Bord G´a is for their excellent advice and feedback.1attributes.The Roughfield in the North Sea has a deliverability of455GWh (1.5billion cubic feet)of gas per day and a total storage capacity of30TWh (100billion cubic feet)of natural gas at pressures of over200bar.It is currently the largest gas storage facility in the UK,able to meet approximately10%of current UK peak day demand.We examine the problem faced by owners of gas storage contracts of how to inject and withdraw natural gas in an optimal manner so that gas is injected when prices are lowest and withdrawn when prices are high.Storage contracts are typically of twelve months duration and the storage operators must be in-formed at the outset of each day whether they should inject or withdraw gas on that day.Gas prices exhibit a noticeable seasonality each year.We fo-cus upon the northern European market where prices drop in the summer as consumption for heating purposes decreases and rise in the winter as temper-atures drop.We model gas prices using a stochastic process and determine the expected-profit maximizing injection/withdrawal for an energy trader who wishes to decide whether to inject or withdraw gas for that day[5].The theory of real options is based on the realization that many business decisions have properties similar to those of derivative contracts used infi-nancial markets[11].A natural gas well can be thought of as a series of call options on the price of natural gas,where the strike or exercise price is the total operating and opportunity costs of producing gas[10],if we ignore operating characteristics.By operating a gas storage facility in the way that maximizes the expected cashflow with respect to the market’s view of future uncertainties and its risk tolerances for those uncertainties,one can subsequently maximize the market value of the facility itself.This paper is structured as follows:Section2presents a stochastic model for gas prices and optimization of the storage facility.It also discusses the depen-dencies of the model and the numerous input parameters that contribute to the complexity of the problem.Section3presents the optimization model required to solve the injection/withdrawal schedule for each Monte-Carlo simulation.A software application for energy traders that facilitates model configuration, solving and presentation of results in a graphical manner is also presented.We also discuss possible extensions in Section4before concluding.2Gas Storage ModelIn this section we outline the relevant inputs for the problem,present an equa-tion for determining inventory levels and describe the stochastic model we use to represent gas price movements.Difficulties arise when operating characteristics and extreme pricefluctua-tions are included in a pricing model[1].The exotic nature of storage facilities and gas prices requires the development of complex methodologies both from the theoretical as well as the numerical perspective.The operating character-istics of actual storage facilities pose a challenge due to the non-trivial nature of the opportunity cost structure.When gas is withdrawn from storage the gascannot be released again.Also,when gas is released the deliverability of the remaining gas in storage decreases because of the drop in pressure.Similarly, when gas is injected into storage both the amount and the rate of future gas injections are decreased.The opportunity costs and thus the exercise price varies nonlinearly with the amount of gas in the reservoir[7].These facts,coupled with the complicated nature of gas prices,have serious implications for numerical valuation and control.There are three common numerical techniques that could be used when determining the value of a real-option to store or withdraw gas on a given day:•Monte-Carlo simulation,•binomial/trinomial trees,•numerical partial differential equation(PDE)techniques.Monte-Carlo simulation is the mostflexible approach because it can handle a wide range of underlying uncertainties.However,it is not ideal for han-dling problems for which an optimal exercise strategy needs to be determined exactly,and in particular when that strategy may be non-trivial.Although im-perfect,because of the inaccuracies,this approach is very popular because it is computationally tractable and accuracy can be improved by allowing more sim-ulations.Price spikes should be an integral part of any gas market model and Monte-Carlo simulations are the most robust means of replicating such price behavior.Closed form solutions cannot in general cater for such spikes because the techniques are based on calculus and require continuous and differentiable functions representing price movements.For these reasons we investigate the use of Monte-Carlo simulation and present a model for optimizing the injec-tion/withdrawal schedule for storage users.2.1The input parametersLet us begin by defining the seven relevant variables and parameters.Figure1 presents a schematic diagram that illustrates how these variables affect the gas store.Let•P=the price per unit of natural gas.•I=the amount of working gas inventory.•c=the control variable representing the amount of gas currently being released(c>0)or injected(c<0).•I max=the maximum storage capacity of the facility.•c max(I)=the maximum deliverability rate(as a function of inventory levels).•c min(I)=the maximum injection rate(c min(I)<0)as a function of inventory levels.Figure1:The parameters for measuring the performance of a gas storage facility.•a(I,c)=the amount of gas lost given c units are being released/injected.The objective is to maximize the expected overall cashflow.The cashflow at any timeτin the future is(c−a(I,c))P,i.e.the amount of gas bought or sold times the price P.This cashflow in the future is worth e−ρτ(c−a(I,c))P now,whereρis the current interest rate. The sum of all cashflows ismax c(P,I,t)ETe−ρτ(c−a(I,c))Pδτ,(1)subject to c min(I)≤c≤c max(I).2.2Equations for I and PWe can easily deduce that the change in I depends on c and a(I,c):dI=−(c+a(I,c))dt.The decrease in inventory is just the sum of gas being extracted,c,and lost through pumping/leakage,a(I,c).Natural gas prices can exhibit extreme price fluctuations unlike those of virtually all other commodities,partially due to imperfections in the storage market.Prices may jump orders of magnitude in a short period of time and then return to normal levels just as quickly.Figure2 illustrates some of the extreme price movements that are not unusual either in American or European gas markets.A normal level varies depending upon the time of year.No generally agreed upon stochastic model exists for natural gas prices(some non-price-spike models can be found in[8]).Hull also gives anFigure 2:Natural gas prices.overview of stochastic processes for natural gas [6].A general valuation and control algorithm must be flexible enough to deal with a wide range of potential spot price models while remaining computationally tractable.We use P to denote the gas prices and changes in the price,dP are as follows:dP =µ(P,t )dt +σ(P,t )dX 1+N k =1γk (P,t,J k )dq k ,(2)where,•µ,σand the γk ’s (all N of them)can be any arbitrary functions of price and/or time.•dX 1is the standard Brownian motion increment.•The J k ’s are drawn from some other arbitrary distributions Q k (J ).•dq k ’s are Poisson processes with the properties:dq k = 0with probability 1− k (P,t )dt 1with probability k (P,t )dt ,In the above mean-reverting model we let the mean,µ1(P,t )dt =α(A +βA ∗Cos (2π(t 365−t A 365))+βSA ×cos (4π(t −t SA 365))−S t ),(3)so that we model gas prices that incorporate annual and semi-annual peaks.Via calibration of historical natural gas prices in the UK over eight years1,we found that A=29.2269,βA=9.8169,t A=−28.4464,βS A=−4.2561,t SA=47.0376. We found thatσ≈0.4.We defined upward jumps as>0.40and downward jumps as<0.20and these are modelled separately to diffusion.The relative frequencies of jumps up and down for the months January to December are as follows:•{4,2,2,2,1,1,0,3,3,6,3,7},•{6,5,2,2,0,0,3,3,2,4,0,8}.The mean jump sizes are0.725966106and-0.400104082and the standard de-viation for these jumps are0.297140631and0.098653958,respectively.The Poisson processes simulate price spikes,or sudden large jumps,that often occur in gas prices because of interruptions in supply or sudden peaks in demand.Multiple(N)Poisson processes can simulate different types of ran-dom events that may cause such jumps,itary conflict,extreme weather conditions,supply failures etc.With probability k(P,t)dt,dq k=1,and P increases by an amountγk(P,t,J k),where J k is drawn from some distribu-tion Q k(J).The addition of more Poisson processes has the benefit of not substantially increasing the computational complexity.There are a large number of parameters required as input into the model.A liquid secondary derivatives market is very useful in parameter estimation and spot model validation.Additional random factors may be added,that include stochastic volatility,stochastic mean reversion,path dependent models (price dynamics can depend on the current total amount in storage)Given representations for the dynamic processes,governing inventory,I,and price, P,we can set out to derive equations for the corresponding optimal strategy c(P,I,t)and the corresponding optimal value V(P,I,t).This can be achieved using different choices of stochastic models for gas prices.3Injection/Withdrawal Scheduling Problem Given a single Monte-Carlo price simulation,we inspect the generated prices and optimize the injection/withdrawal sequences retrospectively.We have an-ticipated gas prices for a given number of days in the future up until the end date of the storage contract.So,for example,at the beginning of a one-year contract there would be365simulated prices for the forthcoming year.2There remains the problem of deciding the optimal injection/withdrawal schedule for this simulated price movement.The trader has a choice of three actions for each day of the year,inject,withdraw or do nothing.In the northern hemisphere the critical decision times during the year occur in September-November and January-March.At other times of the year the price is usually so low or so high that injection and withdrawal decisions can be made easily.1(Prices were taken from31/01/98to31/01/06.2Storage contracts are usually of one year duration and begin on the1st of April.Energy traders in gas supply companies make decisions on a daily basis. They must also bear in mind the duration of their storage contract in this analysis.Storage operators adopt a“use it or lose it”policy with regard to gas that remains in storage after the expiry of the contract.It is therefore ideal to deplete the store at the end of a contract.The following integer linear program formulation represents the profit maximisation problem:maxNi=1p i(dW i W i−dI i I i),(4)where p i is the price on day i,W i is the maximum withdrawal amount on day i,I i is the maximium injection amount on day i,dW i is the decision variable on whether to inject or not,dI i is the decision variable on whether to withdraw on day i or not.Injection and withdrawal are mutually exclusive decisions,therefore,dI k+dW k≤1,∀k=1...N.(5) Also,there cannot be a negative amount of gas in storage on any given day in the future,j,so the following capacity constraints apply:INV+jk=1dI k I k−dW k W k≥0,∀j=1...N,(6)where INV is the amount stored in the facility at the start of the contract. In many cases this is0,because contracts usually involve a“use it or lose it”policy.Similarly,we cannot exceed our maximum capacity on any day,j:INV+jk=1dI k I k−dW k W k≤MAXCAP,∀j=1...N,(7)where MAXCAP is the maximum storage capacity as agreed in the contract. In this model there are2N variables and2N constraints,where N is the number of days remaining in the contract.The decisions on injection or withdrawal are made at the beginning of each day and cannot be reversed,thus imposing integrality constraints on the decision variables.This problem is N P-hard.Using the lp solve ILP solver[2],we attempted to solve instances of this problem.Unfortunately,some individual instances can take in the order of several minutes to solve optimally using branch and bound search3.Recall that we are adopting a Monte-Carlo simulation approach to determine the optimal strategy over a set of many possible instances.We repeat the price simulations many times so that we can gain confidence in our withdrawal or injection decisions.In terms of usability,the energy traders also require a response from the system within at mostfive minutes because decisions on withdrawal or injection are made early in the morning of each day and there is a tight deadline on decision times.3These experiments were conducted using a1.8GHz Intel Pentium III CPU processor.3.1Solution TechniqueTo aid computability,we relax the integrality constraints on the injection and withdrawal decision variables.In operational terms,this means that we ignore the fact that decisions can only be made at the start of the day.The linear relaxation assumes that a single withdrawal or injection decision can be made at any time during the day.This alteration allows us to solve the model in polynomial time and speeds up the computation by two orders of magnitude. Wefind that,on average,less than2%of the decision variable solution values are fractional.This provides strong evidence that our approximate solution technique does not seriously affect our results.The main reason for the low number of fractional values is that our price simulations do not provide intra-day price movements and only generate a single opening price for each day in the future.This level of granularity is deemed sufficient by energy traders.The linear relaxation of each optimization problem,involving a single price simulation for the remainder of the contract,is solved optimally in turn.The results of dW i and dI i are then averaged in order to determine what the best decision for day i is likely to be.Given that gas suppliers can decide daily on their injection policy,only the decision for today is required to know what needs to be done for that day.Nevertheless,energy traders can see the probability of certain strategies being optimal for future days given the status quo.This helps with budgeting and planning for the gas supplier.We conducted experiments to determine the scalability of this approach.In a worst case situation,where N=365,over300simulations can be solved in just over4minutes.Energy traders informed us that this does not pose a problem.In practice,the software tool is most useful in autumn and spring.This is because storage contracts begin in April and,therefore,the decision to inject dominates in thefirst3-4 months of the contract.It is principally used when there are less than220 days remaining in the contract,in which case the problems can be solved much faster.In experiments we found that the total runtime,t,is proportional to the square of the the number of days remaining in the contract,t∝N2.3.2Software ApplicationIt is important that the complexities of mathematical model and Monte Carlo simulation technique are abstracted away from the end-user.We designed a user interface that permits the entry of all necessary variables and illustrates the output in a graphical manner that is easy to understand and requires little or no training for the energy traders.Figure3presents a snapshot of the gas storage optimisation application[4]. The user can choose to set the following parameters on the Settings Menu:•Expected DIAF(Daily Injection AdjustmentFactors):The expected DIAFs for dates in the future are issued by the storage operator can be viewed/updated by selecting any day onFigure3:User Interface.the calendar4.These values indicate how rapidly one may inject or withdraw gas from the facility and are a function of the pressure within the underground cavern.They are thus a function of the behaviour of all other gas companies with storage contracts.Thesefigures depend upon the pressure within the storage facility.Figure4shows the interface for updating DIAFs.•Contract Details:The size of the storage facility can be updated here.The contract start and end-dates can also be modified.•Simulation Preferences:This window displays the form of the stochas-tic process used for simulations.The number of desired simulations can also be updated here.More simulations imply greater accuracy in the predictions.Once all the parameters in the dropdown menus are chosen,the current price of gas and the inventory in storage can be selected on the main screen. The“Launch Simulation”button can then be clicked to simulate the price pro-cesses.The status bar,at the bottom of the page,initially indicates that theses simulated prices are being generated.Then,the optimisation software deter-mines optimal solutions for each simulation.This is computationally intensive 4These values can be set to zero during downtime or a force majeure that precludes injection/withdrawal until this time.Figure4:DIAF updates.and the application absorbs most of the CPU’s processing capabilities during this procedure.The runtime grows as a square of of the remaining days to the contract end-date.Obviously,it grows linearly in the number of simulations requested.The graph entitled“Injection/Withdrawal”plan is updated in real-time as more simulated problems are solved.This dynamic behaviour allows energy traders to visually assess whether the number of simulations provided results in a stable expected schedule.If,nearing the end of the run,the graph is still changing significantly between simulations,this means that more simula-tions are ually after approximately150simulations the expected schedule and profitability graphs begins to settle and smoothen.The graph itself can be interpreted as follows.The abscissa indicates the dates,from today on the far left to the end date of the contract on the far right.The ordinate data represents the probability that a certain policy will be the optimal decision given possible future events.Red indicates injection, blue is withdrawal and yellow means do nothing.In Figure3we see that100 simulations were performed on the6th/May2006.The schedule for the coming days indicates yellow with100%probability.This was because the facility was closed for several weeks for repairs.The DIAFs were set to zero to indicate this event.Upon re-opening,the likely optimal strategy is to inject with82% probability.Injection is likely to continue until early October.The graph entitled“Max-Profit Probability Distribution”indicates the prob-ability distribution of profits that could be made from the store and its inven-tory,given perfect foresight about future prices.This,therefore,reflects a distribution over the upper bounds on valuations for the storage contract and the gas in storage.The series of graphs entitled“Price Distribution(Day X)”demonstrate the anticipated price movements for gas given the current price and date.The probability distribution over future days can be viewed by selecting the“Next Day”button.Gas suppliers can decide daily on their injection policy,so onlydI1and dW1(see Section2)are required to inform the user of the optimal expected policy for that day.This information is given in the results box with“Today’s Recommended Action”.The confidence rating indicates the probability that this decision is optimal over the given number of simulations.3.3Results and Feedback from Energy TradersOur results and subsequent discussions with energy traders were extremely positive.They found the interface easy to use and the outputs can be clearly interpreted.But most importantly,the software application is performing ex-tremely favourably when compared to human decision making.It is used on a daily basis be energy traders as a decision support system.The software was crucial in pointing out some anomalies in human behaviour.Traders were not compensating sufficiently for the adjustments in the DIAF,the amount of gas that can be injected or withdrawn on a daily basis.Instead,the focus remained toofirmly on the the gas price.The optimisation model showed that it is better to withdraw earlier in the season whilst other competitors are still injecting to avail of the higher pressure.It also highlights the game-theoretic aspects of the storage market and how the sub-optimal behaviour of competitors in the market can be explotied.We also discovered that the do-nothing policy is overlooked too often by traders and should be adopted more frequently.One possible explanation is that traders who choose to not to inject or withdraw on a given day may be sub-ject to criticism from those who perceive the the facility as being under-utilised, when in fact either injecting or withdrawing can harm expected profitability. This tool helps to illustrate how in certain circumstances,a policy of inaction is best.For example,consider a scenario when prices are rising,it is near the end of the contract and there is little gas in storage.It may be best to wait and withdraw tomorrow when prices will be higher and you can maintain higher deliverability also.Figure5demonstrates the aggregate runtimes for300simulated scheduling problems using the linear relaxation technique.It is approximately quadratic in the number of days remaining in the contract.Energy traders indicated that this is perfectly acceptable for their needs.Another worthwhile result of this endeavour was the increased interest in the potential potential applications of Artificial Intelligence within the natural gas sector.Bord G´a is is now the official sponsor a taught Masters programme in Intelligent Systems and students will conduct research projects in conjunction with this company in future years.4Possible ExtensionsSome of the following additional features could also enhance the system so that accuracy and performace may be improved:1.Incorporate forward/future prices to determine expected volatility,1 1010010000 50 100 150 200 250300 350 400T i m e (s e c s )Days remaining in the storage contract Figure 5:Scalability of LP approach.2.Incorporate risk aversion into the optimisation model,3.Incorporate interest rates to model discounting of future income and ex-penditure.4.LP model can be improved through removal of implicit constraints.Thereis no need for maximum capacity constraints at the beginning of thestorage contract.5.Determination of optimal control policy given multiple storage facilities.We are also examining another related problem that presents computational problems for the operator of the storage facility.Recently,there have been op-erational difficulties with storage facility that caused a prolonged downtime [3].This was caused be a fire and has increased awareness of safety issues.Main-tenance and the scheduling of downtime is gaining greater priority.We plan to use a constraint programming model that incorporates global constraints that enforce a minimum number of do-nothing events whose scheduling on consec-utive days facilitates cost-minimising maintenance.Another interesting line of research may involve the game theoretic study of equilibrium behaviour in this market.Given that the DIAFs are determined by the pressure within the storage facility,competing gas utilities with storage contracts directly affect the rate at which others can inject or withdraw gas.5ConclusionStochastic optimisation of gas storage facilities enables gas suppliers to schedule injection and withdrawal over the duration of a storage contract in a manner that maximises expected profitability.We presented a mean-reverting price model that incorporates diffusion and jump components.We then presented an ILP formulation of the injection/withdrawal scheduling problem.We found that it was necessary to adopt the linear relaxation of this model so that we cansolve hundreds of simulated price movements over the remainder of the storage contract in a timely manner.We showed that in practice fractional solutions have a very small impact on solution accuracy.This solution has been deployed very successfully and is used regularly by energy traders.This project has been so well received that the company are now in the process of introducing various AI techniques to assist in other areas of the business.References[1]Hyungsok Ahn,Albina Danilova,and Glen Swindle.Storing arb.Wilmott,1,2002.[2]Michael Berkelaar,Kjell Eikland,and Peter Notebaert.lp solve version5.0.10.0./group/lp_solve/.[3]Centrica.Force majeure update12th may2006.http://www.centrica-/Storage/MediaPress/Incident20060216q.html,May2006.[4]Alan Holland.Stochastic optimization for a gas storage facility.Demon-stration Session,Principles and Practices of Constraint Programming(CP-2006),September2006.[5]Alan Holland.Injection/withdrawal scheduling for natural gas storagefacilities.In Proceedings of the ACM Symposium on Applied Computing (ACM-EC2007),2007.[6]John C.Hull.Options,Futures and Other Derivatives.Prentice-Hall,2003.[7]Mike Ludkovski.Optimal switching with application to energy tolling agree-ments.PhD thesis,Princeton University,2005.[8]Dragana Pilipovi´c.Energy Risk:Valuing and Managing Energy Deriva-tives.McGraw-Hill,1998.[9]Ken Silverstein.More storage may be key to managing natural gas prices.PowerMarketers Industry Publications,October2004.[10]Matt Thompson,Matt Davison,and Henning Rasmussen.Natural gasstorage valuation and optimization:A real options application.preprint.[11]Paul Wilmott.Paul Wilmott introduces Quantitative Finance.Wiley,2001.。
优化存货管理模式的方法
优化存货管理模式的方法Optimizing inventory management is crucial for any company looking to reduce costs, improve efficiency, and maximize profits. 优化存货管理对于任何希望降低成本、提高效率并最大化利润的公司来说至关重要。
Effective inventory management involves finding the right balance between maintaining enough stock to meet customer demand and minimizing excess inventory that ties up capital. 有效的存货管理涉及找到保持足够库存以满足顾客需求和最小化过剩库存以释放资金之间的平衡。
This delicate balance can be achieved through various strategies and technologies that streamline the inventory management process. 这种微妙的平衡可以通过各种策略和技术来实现,这些策略和技术会简化存货管理过程。
One way to optimize inventory management is to implement a just-in-time (JIT) inventory system. 实施及时制 (JIT) 存货系统是优化存货管理的一种途径。
This system involves ordering inventory only when it is needed, which helps to reduce excess inventory and storage costs. 这种系统只有在需要时才订购存货,这有助于减少过剩库存和储存成本。
写裁剪节约成本方案的作文
写裁剪节约成本方案的作文英文回答:Cost saving is one of the key priorities for businesses in today's competitive market. One effective way to reduce costs is through the implementation of a cutting and trimming plan. By optimizing the use of resources and materials, businesses can significantly reduce their production costs and increase their profitability.Firstly, businesses can consider implementing a material optimization strategy. This involves carefully analyzing the production process and identifying areas where materials are being wasted. By reducing material waste and optimizing the use of raw materials, businesses can save on procurement costs and minimize the environmental impact of their operations.Secondly, businesses can explore the option of outsourcing the cutting and trimming process to specializedservice providers. By leveraging the expertise andresources of external partners, businesses can benefit from cost-effective solutions and access to advanced cutting and trimming technologies. This can result in significant cost savings and improved efficiency in the production process.Furthermore, businesses can invest in modern cuttingand trimming equipment to enhance their in-house operations. Advanced machinery and technology can help businesses achieve higher precision and productivity, leading to reduced production time and lower operating costs. Additionally, businesses can also consider training their workforce to operate the new equipment effectively,ensuring optimal performance and cost savings in the long run.In conclusion, implementing a cutting and trimming plan can be an effective strategy for businesses to reduce costs and improve their bottom line. By optimizing materials, outsourcing to specialized service providers, and investing in modern equipment, businesses can achieve significantcost savings and enhance their competitive edge in themarket.中文回答:节约成本是当今竞争激烈市场中企业的关键优先事项之一。
发掘成本低减方案 英语
发掘成本低减方案英语英文回答:Cost Reduction Initiatives.In today's competitive business environment, organizations are constantly seeking ways to reduce costs and improve profitability. Implementing cost reduction initiatives can be a challenging task, but it is essential for businesses to remain viable in the long run.There are many different approaches to cost reduction, and the best approach will vary depending on the specific circumstances of an organization. However, some common strategies include:Reviewing and streamlining processes: Identifying and eliminating unnecessary or inefficient processes can lead to significant cost savings. This can involve automating tasks, simplifying workflows, and consolidating functions.Negotiating with suppliers: Renegotiating contracts with suppliers can often result in lower prices for goods and services. It is important to have a strong understanding of the market and to be prepared to walk away from a deal if the supplier is unwilling to negotiate.Reducing inventory: Holding excess inventory can tie up capital and lead to increased storage costs. Implementing inventory management strategies, such as just-in-time inventory, can help to reduce inventory levels and free up cash flow.Outsourcing non-core functions: Outsourcing certain tasks or functions to third-party providers can be a cost-effective way to improve efficiency and reduce overhead costs.Investing in technology: Implementing new technologies can lead to cost savings by automating tasks, improving communication, and enhancing productivity. However, it is important to carefully evaluate the potential return oninvestment before making any significant technology investments.In addition to these specific strategies, there are also a number of general principles that can help organizations to achieve cost reductions. These principles include:Focusing on the big picture: When evaluating cost reduction initiatives, it is important to consider the overall impact on the organization. Short-sighted cuts can lead to long-term problems.Empowering employees: Giving employees the authority to make decisions and find ways to reduce costs can lead to significant savings.Setting realistic goals: Setting unrealistic cost reduction goals can lead to disappointment and frustration. It is important to set achievable goals that can be sustained over time.Measuring and tracking results: It is essential totrack the results of cost reduction initiatives to ensure that they are achieving the desired outcomes. This willhelp to identify areas for improvement and to make necessary adjustments.实施成本削减计划是一项具有挑战性的任务,但它对于企业长期保持活力至关重要。
英文外贸函电降成本作文
英文外贸函电降成本作文Title: Strategies to Reduce Costs in Foreign Trade Correspondence。
In the competitive landscape of international trade, managing costs effectively is crucial for sustained profitability and growth. One area where businesses can optimize expenditures is in foreign trade correspondence. Here are several strategies to reduce costs in this aspect:1. Utilize Electronic Communication: Embracing digital communication channels such as email, instant messaging,and video conferencing can significantly reduce the expenses associated with traditional mail, courier services, and long-distance phone calls. Moreover, electronic communication facilitates real-time interaction, expediting decision-making processes and enhancing overall efficiency.2. Standardize Templates and Processes: Developing standardized templates for common types of correspondence,such as inquiries, quotations, and order confirmations, streamlines the drafting process and ensures consistency across communications. Additionally, establishing standardized processes for handling inquiries, resolving disputes, and managing documentation reduces the time and resources required to complete tasks, thus driving down costs.3. Implement Translation Tools: Investing in translation software or utilizing online translation services can help mitigate the expenses associated with professional translation services. While automated translation may not always be perfect, it can suffice for routine correspondence and basic communication needs, thereby reducing reliance on costly language experts.4. Centralize Communication Platforms: Consolidating all foreign trade correspondence onto a single platform or system centralizes communication channels, simplifies management, and reduces overhead costs associated with maintaining multiple platforms or subscriptions. This centralized approach also enhances visibility and controlover communication activities, facilitating betteroversight and coordination.5. Negotiate Bulk Discounts: When engaging with service providers for communication-related services such as email hosting, messaging platforms, or translation services, negotiate bulk discounts based on anticipated usage volumes. Leveraging economies of scale through strategicpartnerships can yield significant cost savings over time.6. Train Staff on Efficient Communication Practices: Providing training to employees on efficient communication practices, including email etiquette, effective use ofdigital tools, and cross-cultural communication skills, enhances productivity and reduces the likelihood of costly errors or misunderstandings. Well-trained staff are better equipped to handle communication tasks efficiently, minimizing the need for costly revisions or clarifications.7. Optimize Document Management Systems: Implementing robust document management systems that enable efficient storage, retrieval, and sharing of correspondence andrelated documents can streamline workflows and reduce administrative overhead. By digitizing and organizing documents systematically, businesses can minimize the time and resources spent on manual filing and retrieval processes.8. Monitor and Analyze Communication Costs: Regularly monitor and analyze communication-related expenses to identify cost-saving opportunities and areas for optimization. By tracking key metrics such as per-unit communication costs, response times, and customer satisfaction levels, businesses can identify trends, pinpoint inefficiencies, and implement targeted cost-reduction initiatives.In conclusion, by adopting a proactive approach to managing foreign trade correspondence, businesses can effectively reduce costs while maintaining high standards of communication quality and efficiency. Embracing digital technologies, standardizing processes, leveraging automation, and optimizing resource allocation are keystrategies for achieving cost savings in this critical aspect of international trade.。
组织结构优化的英语作文
组织结构优化的英语作文Title: Optimizing Organizational Structure。
In today's dynamic and competitive business environment, organizations constantly seek ways to enhance their efficiency and effectiveness. One such strategy isoptimizing organizational structure. By reevaluating how tasks, roles, and responsibilities are distributed, companies can streamline operations, improve communication, and ultimately achieve better results. In this essay, wewill explore the importance of optimizing organizational structure and discuss strategies for its implementation.First and foremost, optimizing organizational structure fosters agility and adaptability. As markets evolve and customer preferences shift, companies must be able to respond swiftly to changes. A hierarchical and rigid structure can hinder this agility, as decision-making processes become slow and bureaucratic. By flattening the hierarchy and empowering employees at all levels,organizations can decentralize authority and enable quicker responses to market demands.Moreover, optimizing organizational structurefacilitates better communication and collaboration. In traditional hierarchical setups, information flow is often restricted, leading to silos and departmental rivalries. However, by adopting a more matrix or network-based structure, companies can break down these barriers and encourage cross-functional collaboration. This not only enhances innovation and problem-solving but also cultivates a sense of shared purpose among employees.Furthermore, optimizing organizational structure can improve resource allocation and cost efficiency. In many cases, companies with bloated bureaucracies suffer from redundancy and inefficiency, wasting valuable resources. Through streamlining processes and eliminating unnecessary layers of management, organizations can reduce overhead costs and redirect resources towards strategic initiatives. This not only improves financial performance but also enhances competitiveness in the market.However, while the benefits of optimizingorganizational structure are evident, implementing such changes can be challenging. Resistance to change, ingrained organizational culture, and fear of job loss are common obstacles that must be overcome. Therefore, effective change management strategies are essential to ensure a smooth transition. This includes clear communication, employee training, and involving stakeholders in the decision-making process.One effective approach to optimizing organizational structure is the use of technology. With the advancement of digital tools and platforms, companies can automate routine tasks, enhance data analytics, and facilitate remote collaboration. For example, cloud-based project management software allows teams to coordinate efforts seamlessly, regardless of geographical location. Similarly, artificial intelligence and machine learning algorithms can optimize workflows and improve decision-making processes.Another strategy is to foster a culture of continuousimprovement. Rather than viewing organizational structure as a static framework, companies should regularly evaluate its effectiveness and make adjustments as needed. This requires a mindset shift towards experimentation and learning from failures. By encouraging employees to challenge the status quo and explore new ways of working, organizations can stay ahead of the curve and adapt to changing market dynamics.In conclusion, optimizing organizational structure is essential for modern businesses to thrive in a rapidly evolving landscape. By promoting agility, enhancing communication, and improving resource allocation, companies can achieve sustainable growth and maintain a competitive edge. However, successful implementation requires overcoming resistance to change and leveraging technology effectively. Ultimately, organizations that embrace flexibility and innovation will emerge as leaders in the global marketplace.。
全过程造价控制的合理调整建议英文版
全过程造价控制的合理调整建议英文版Suggestions for Reasonable Adjustment of Whole Process Cost ControlIn the current business environment, effective cost control is crucial for the success of a project. Here are some practical suggestions for adjusting the whole process cost control:1. Establish Clear Objectives: Define the project's goals and objectives clearly to ensure that cost control measures are aligned with the overall project strategy.2. Regular Monitoring and Reporting: Implement a system for regular monitoring of costs and progress, and ensure that reports are generated and shared with relevant stakeholders in a timely manner.3. Identify Cost Drivers: Analyze the key factors driving costs in the project and focus on managing those aspects to achieve cost savings.4. Streamline Processes: Identify and eliminate any redundant or inefficient processes that may be contributing to unnecessary costs.5. Vendor Management: Negotiate with vendors to secure the best possible prices for goods and services, and establish long-term partnerships based on mutual benefit.6. Risk Management: Anticipate potential risks that could impact costs and develop contingency plans to mitigate these risks effectively.7. Training and Development: Invest in training and development programs for employees to enhance their skills and knowledge, which can lead to improved efficiency and cost savings.8. Technology Integration: Utilize technology solutions to automate processes, improve accuracy, and reduce manual labor costs.9. Benchmarking: Compare project costs to industry standards and benchmarks to identify areas where costs can be reduced without compromising quality.10. Continuous Improvement: Encourage a culture of continuous improvement within the organization, where employees are empowered to suggest and implement cost-saving initiatives.By implementing these suggestions, project managers can make reasonable adjustments to the whole process cost control, leading to improved efficiency, reduced waste, and ultimately, better project outcomes.。
精益生产之降低设设定时间-Setup_Reduction(中英文对照)
Job
Production
#1
Downtime
Job #2
Ramp-up time
Analyze
• Propose Critical X’s • Prioritize Critical X’s • Verify Critical X’s • Estimate the Impact of
Each X on Y • Quantify the Opportunity • Prioritize Root Causes • Conduct Root Cause
Measure Analyze Improve Control
Lean Six Sigma
总览
Control
What Is Set-Up Reduction?
Set-Up Reduction is a process in which the time and effort for equipment changeover is reduced to an absolute minimum without adversely affecting quality.
Revised 1-12-02
Setup Reduction
• Cp & Cpk • SupplyChainAccelerator
Analysis • Multi-Vari • Box Plots • Interaction Plots • Regression • ANOVA • C&E Matrices • FMEA
organizational structure adjustment announcement
organizational structureadjustment announcementOrganization Structure Adjustment AnnouncementDear all,We are pleased to announce that our company will be undergoing an organizational structure adjustment to better align with our strategic goals and enhance our operational efficiency. This adjustment is a crucial step towards our future development and will enable us to better serve our customers and stakeholders.The adjustment will involve the following changes:1. Restructuring of departments and teams to enhance collaboration and streamline processes.2. Alignment of roles and responsibilities to ensure clear accountability and efficient decision-making.3. Optimization of resource allocation to support the growth of our core businesses.We understand that this may bring about some changes and transitions, but we are confident that these adjustments will ultimately lead to a more agile and competitive organization. Throughout this process, our top priority remains the well-being and development of our employees. We will do our best to support everyone through the transition and ensure a smooth integration into the new structure.We believe that this organizational structure adjustment will position us for long-term success and we look forward to working together with you to achieve our shared goals. If you have any questions or concerns, please do not hesitate to reach out to your managers or the human resources department.Thank you for your continued dedication and support.Best regards,Management Team。
减少工厂规模英语作文
减少工厂规模英语作文Title: Strategies for Downsizing Factory Operations。
In today's dynamic business environment, companies often face the need to downsize their factory operationsfor various reasons such as cost reduction, market changes, or optimization of resources. While this decision may be challenging, implementing it effectively is crucial for maintaining competitiveness and sustainability. In this essay, we will explore strategies for downsizing factory operations.First and foremost, clear communication is paramount when initiating the downsizing process. It's essential to communicate transparently with employees about the reasons behind the decision, the expected changes, and the support available to them during the transition period. Open dialogue can help alleviate anxiety and foster a sense of trust, which is vital for maintaining morale and productivity amidst uncertainty.Secondly, strategic planning plays a crucial role in the downsizing process. Companies should conduct a comprehensive analysis of their current operations, identifying areas where downsizing can be implemented without compromising core functions or customer satisfaction. This may involve evaluating production processes, identifying redundant tasks or departments, and assessing the impact of downsizing on overall performance.Moreover, companies should prioritize retaining key talent during the downsizing process. Talented and skilled employees are invaluable assets to any organization, and losing them can have long-term repercussions. Offering incentives such as retention bonuses, career development opportunities, or flexible work arrangements can help retain top performers and mitigate the loss of talent.Additionally, investing in retraining and reskilling programs can help mitigate the negative impact of downsizing on employees. Providing training opportunities for displaced workers to acquire new skills or transitionto different roles within the company can enhance their employability and facilitate a smoother transition to new employment opportunities.Furthermore, companies should explore alternatives to layoffs wherever possible. Implementing measures such as hiring freezes, voluntary retirement programs, or reduced work hours can help minimize the need for involuntary layoffs and mitigate the impact on employee morale and company culture.Moreover, companies should prioritize maintaining a positive employer brand throughout the downsizing process. How a company handles downsizing can significantly impact its reputation in the eyes of current and prospective employees, customers, and other stakeholders. Treating employees with respect, providing support during the transition, and being transparent about the company's future plans can help preserve goodwill and mitigate negative perceptions.In conclusion, downsizing factory operations is achallenging but sometimes necessary step for companies to adapt to changing market conditions and remain competitive. By implementing clear communication, strategic planning, talent retention efforts, retraining programs, alternatives to layoffs, and maintaining a positive employer brand, companies can navigate the downsizing process more effectively while minimizing the negative impact on employees and the organization as a whole.。
调整方法 英语作文
调整方法英语作文Title: Techniques for Adjusting to Change。
Change is an inevitable aspect of life, and learning to adapt to it is crucial for personal growth and success. Whether it's a change in circumstances, environment, or mindset, employing effective adjustment techniques can make the transition smoother. In this essay, we will explore various strategies for adapting to change.First and foremost, maintaining a positive attitude is paramount when facing change. Embracing the mindset that change brings opportunities for growth rather than obstacles can significantly impact how one navigates through transitions. By focusing on the potential benefits of change, individuals can approach new situations with optimism and resilience.Secondly, staying flexible and open-minded is essential for adapting to change. Rigidity can hinder one's abilityto adjust, whereas flexibility allows for greater adaptability. Being open to new ideas, perspectives, and experiences enables individuals to explore different possibilities and find innovative solutions to challenges presented by change.Furthermore, effective communication plays a crucial role in adjusting to change. Expressing concerns, seeking support, and sharing ideas with others can provide valuable insights and perspectives. Additionally, effective communication fosters collaboration and teamwork, which are essential for navigating through periods of change successfully.Another important technique for adjusting to change is setting realistic goals and priorities. Identifying what is most important during times of transition helps individuals focus their efforts and resources where they are needed most. By setting achievable goals and establishing clear priorities, individuals can maintain a sense of direction and purpose amidst change.Moreover, practicing self-care is vital for managing stress and maintaining well-being during times of change. Engaging in activities that promote physical, emotional, and mental health, such as exercise, meditation, and spending time with loved ones, can help individuals cope with the challenges of change more effectively.Additionally, seeking opportunities for learning and growth can facilitate adjustment to change. Viewing change as a chance to acquire new skills, knowledge, and experiences fosters personal development and adaptation. By embracing learning opportunities, individuals can expand their capabilities and enhance their resilience in the face of change.Lastly, maintaining a sense of perspective andgratitude can help individuals navigate through change with grace and resilience. Recognizing the blessings and opportunities amidst challenges fosters a sense ofgratitude and resilience. By focusing on the positive aspects of change and acknowledging one's blessings, individuals can cultivate a mindset of resilience andgratitude that enables them to thrive in the face of adversity.In conclusion, adjusting to change requires a combination of positive attitude, flexibility, effective communication, goal setting, self-care, learning, and gratitude. By employing these techniques, individuals can navigate through transitions with resilience, adaptability, and grace. Embracing change as an opportunity for growth and personal development empowers individuals to thrive amidst uncertainty and adversity.。
国际版移动电池驱动控制器说明书
Portable Battery Operated Controller for International ApplicationsBattery powered wireless interface between a smartphone or tablet and remote electricaldowntilt (RET) devices for setup, optimization, and troubleshootingControl via Wi-Fi connection between RET Master and portable devicesPredefined diagnostic tests for easy and quick troubleshootingEquipped with USB connector for charging handheld devicesCarried from site to site for RET/TMA antenna setup and optimizationOBSOLETEThis product was discontinued on: November 1, 2020Replaced By:ATC200-LITE-USB Portable ControllerProduct ClassificationProduct Type RET controllerGeneral SpecificationsAISG Diagnostic Input Connector8-pin DIN MaleAISG Diagnostic Input Connector Quantity1AISG Output Connector8-pin DIN FemaleAISG Output Connector Quantity1Auxiliary Interface USBAuxiliary Interface Note Used for charging mobile devicesColor Black | YellowCompatible Mobile Devices Android | iOS®DimensionsHeight59 mm | 2.323 inWidth94 mm | 3.701 inLength198.2 mm | 7.803 inElectrical SpecificationsInput Voltage110/240 Vac2.0 A @ 24 V12Page ofPage of 22Output Current at Voltage, continuous2.0 A @ 24 V Output Current at Voltage, maximum2.0 A @ 24 V Output Voltage, typical24 V Electrical Safety StandardCB | CE | IEC 60950-1Interface Protocol SignalData | dc Power SupplyBattery | dc Jack Protocol AISG 1.1 | AISG 2.0Material SpecificationsMaterial Type ABS | Thermoplastic elastomer (TPE)Environmental SpecificationsOperating Temperature0 °C to +50 °C (+32 °F to +122 °F)Relative HumidityUp to 95%, non-condensing Charging Temperature 0 °C to +40 °C (+32 °F to +104 °F)Packaging and WeightsIncludedAdapter (China) | Adapter (Europe) | Adapter (United Kingdom) | Control unit | Power supply | Quick Guide Weight, gross2.1 kg | 4.63 lb Weight, net 1.1 kg | 2.425 lbRegulatory Compliance/CertificationsAgencyClassification CHINA-ROHSBelow maximum concentration value ISO 9001:2015Designed, manufactured and/or distributed under this quality management system REACH-SVHCCompliant as per SVHC revision on /ProductCompliance ROHSCompliant UK-ROHSCompliant。
产业结构失衡英语作文高中
产业结构失衡英语作文高中Imbalance of Industrial Structure。
With the rapid development of the economy, China's industrial structure has undergone significant changes. However, the imbalance of industrial structure has become a prominent issue that needs to be addressed. This essay will discuss the causes and consequences of the imbalance of industrial structure and propose some solutions to this problem.There are several reasons for the imbalance of industrial structure in China. Firstly, the over-reliance on heavy industry has led to the neglect of light industry and the service sector. This has resulted in a lack of diversity in the industrial structure, which makes the economy vulnerable to external shocks. Secondly, the regional imbalance of industrial development has exacerbated the problem. The coastal areas have developed rapidly, while the central and western regions lag behind.This has led to a widening wealth gap and social inequality. Lastly, the excessive consumption of resources and environmental pollution caused by the industrial structure imbalance have posed a threat to sustainable development.The imbalance of industrial structure has had a numberof negative consequences. Firstly, it has hindered the optimization of resource allocation and the improvement of economic efficiency. Secondly, it has led to the deterioration of the environment and the depletion ofnatural resources. This has not only affected the qualityof life of the people, but also hindered the sustainable development of the economy. Lastly, the imbalance of industrial structure has resulted in a lack of competitiveness in the global market, which has hinderedthe overall development of the economy.In order to address the imbalance of industrial structure, several measures can be taken. Firstly, the government should implement policies to promote the development of light industry and the service sector. This can be achieved through tax incentives, financial support,and the removal of barriers to entry. Secondly, efforts should be made to promote the development of the central and western regions. This can be achieved through the implementation of regional development strategies, the improvement of infrastructure, and the provision of financial support. Lastly, the government should implement measures to promote sustainable development, such as the promotion of green technology and the implementation of environmental protection policies.In conclusion, the imbalance of industrial structure is a significant issue that needs to be addressed. It has been caused by the over-reliance on heavy industry, the regional imbalance of industrial development, and the excessive consumption of resources and environmental pollution. The imbalance of industrial structure has had negative consequences, such as the hindrance of resource allocation, the deterioration of the environment, and the lack of competitiveness in the global market. In order to address this issue, the government should implement policies to promote the development of light industry and the service sector, promote the development of the central and westernregions, and promote sustainable development. By taking these measures, the imbalance of industrial structure can be effectively addressed, and the economy can achieve sustainable and balanced development.。
机械设计需要优化的英文词
机械设计需要优化的英文词The Need for Optimization in Mechanical Design.Mechanical design is a crucial aspect of engineering that involves the creation of machines, devices, and systems. It plays a pivotal role in converting theoretical concepts into practical applications. However, to ensure the efficiency, reliability, and performance of these designs, it's imperative to consider optimization. Optimization in mechanical design involves identifying areas of improvement and implementing strategies to enhance the overall design.1. Importance of Optimization.Optimization in mechanical design is essential for several reasons:Efficiency: By optimizing designs, engineers can improve the efficiency of machines and systems, reducingenergy waste and operational costs.Reliability: Well-optimized designs are more likely to withstand wear and tear, ensuring longer service life and reduced maintenance requirements.Performance: Optimization can enhance the performance of mechanical systems, improving their speed, accuracy, and overall output.Safety: By identifying and addressing potential hazards, optimization can help ensure the safety of operators and users.Sustainability: Optimized designs often lead to reduced environmental impact, such as reduced energy consumption and waste generation, aligning with sustainable development goals.2. Areas of Optimization.There are several areas in mechanical design whereoptimization can be applied:Material Selection: Choosing the right material for a particular application can significantly impact the performance and durability of a design. Optimization involves selecting materials that offer the best combination of strength, weight, cost, and durability.Geometric Design: Geometric optimization involves modifying the shape, size, and arrangement of components to improve performance. This can include optimizing theprofile of a gear, the spacing of bearings, or the layout of a mechanical system.Thermal Design: Heat management is crucial in mechanical systems, as excessive heat can lead to performance degradation and failure. Optimization techniques can help identify effective heat dissipation strategies, such as the use of heat sinks or fans.Dynamic Analysis: Analyzing the dynamic behavior of mechanical systems can help identify areas of vibration orresonance that may affect performance. Optimization can involve modifying system parameters to reduce vibrations or improve stability.Control System Design: Optimizing the control system can enhance the precision and responsiveness of mechanical systems. This may involve developing more efficient control algorithms or improving the integration of sensors and actuators.3. Optimization Techniques.There are various techniques and tools available for optimizing mechanical designs:Finite Element Analysis (FEA): FEA is a numerical method used to predict the response of a material or structure to applied loads and boundary conditions. It can help identify stress concentrations, deformation, and other factors that affect the performance of a design.Computational Fluid Dynamics (CFD): CFD is used tosimulate fluid flow within and around mechanical systems.It can help optimize fluid paths, heat transfer, and aerodynamic performance.Optimization Algorithms: These algorithms can search for the best design parameters that maximize performance or minimize cost functions. Techniques such as genetic algorithms, simulated annealing, and gradient-based optimization can be used to find optimal solutions.Design for Manufacturing and Assembly (DFMA): DFMA involves considering manufacturing and assembly constraints during the design phase. It aims to simplify the manufacturing process, reduce costs, and improve assembly efficiency.4. Conclusion.Optimization plays a crucial role in mechanical design, enabling engineers to create efficient, reliable, and sustainable systems. By applying optimization techniques and tools, designers can identify areas of improvement andimplement strategies to enhance the overall performance of their designs. As technology continues to advance, so will the need for optimization in mechanical design, ensuring that machines and systems continue to meet the demands of a rapidly evolving world.。
2024运营优化管理报告英文版
2024运营优化管理报告英文版2024 Operations Optimization Management ReportIn order to achieve our goals for 2024, we must focus on optimizing our operations for maximum efficiency. This report will outline the key strategies and initiatives that will be implemented to enhance our business processes and drive success in the coming year.Key Focus Areas:1. Process Streamlining: We will review and streamline our current processes to eliminate any inefficiencies and bottlenecks that may be hindering our operations.2. Technology Integration: Implementing new technologies and automation tools to improve productivity and accuracy in our day-to-day activities.3. Training and Development: Investing in training programs to upskill our employees and ensure they are equipped with the necessary knowledge and skills to perform their roles effectively.4. Customer Experience: Enhancing the overall customer experience by improving communication channels, resolving issues promptly, and addressing feedback in a timely manner.5. Cost Reduction: Identifying cost-saving opportunities and implementing measures to reduce operational expenses without compromising on quality.Action Plan:- Conduct a comprehensive review of our current processes and identify areas for improvement.- Implement a new project management system to track and monitor project progress more effectively.- Provide training sessions for employees on new technologies and tools that will be implemented.- Develop a customer feedback system to gather insights and drive improvements in our service delivery.- Explore partnerships with vendors and suppliers to negotiate better rates and reduce procurement costs.Timeline:- Q1 2024: Review current processes and identify areas for improvement.- Q2 2024: Implement new technologies and automation tools.- Q3 2024: Conduct training sessions for employees on new tools and processes.- Q4 2024: Evaluate the effectiveness of implemented strategies and make adjustments as necessary.Conclusion:By focusing on optimizing our operations through process streamlining, technology integration, training and development, customer experience enhancement, and cost reduction, we are confident that we will achieve our goals for 2024. Continuous monitoring and evaluation of our strategies will be key to ensuring ongoing success and sustainability in the long run.。
海水淡化新进展--微生物?
Sustainable desalination using a microbial capacitive desalination cell †Casey Forrestal,a Pei Xu b and Zhiyong Ren *aReceived 15th January 2012,Accepted 13th March 2012DOI:10.1039/c2ee21121aMicrobial desalination cells (MDCs)use the electrical current generated by microbes to simultaneously treat wastewater,desalinate water,and produce bioenergy.However,current MDC systems transfer salts to the treated wastewater and affect wastewater’s beneficial use.A microbial capacitivedesalination cell (MCDC)was developed to address the salt migration and pH fluctuation problems facing current MDCs and improve the efficiency of capacitive deionization.The anode and cathode chambers of the MCDC were separated from the middle desalination chamber by two speciallydesigned membrane assemblies,which consisted of cation exchange membranes and layers of activated carbon cloth (ACC).Taking advantage of the potential generated across the microbial anode and the air-cathode,the MCDC was capable of removing 72.7mg total dissolved solids (TDS)per gram of ACC without using any external energy.The MCDC desalination efficiency was 7to 25times higher than traditional capacitive deionization pared to MDC systems,where the volume of concentrate can be substantial,all of the removed ions in the MCDC were adsorbed in the ACC assembly double layer capacitors without migrating to the anolyte or catholyte,and the electrically adsorbed ions could be recovered during assembly regeneration.The two cation exchange membrane based assemblies allowed the free transfer of protons across the system and thus prevented significant pH changes observed in traditional MDCs.IntroductionThe increasing awareness of the water-energy nexus is compelling the development of technologies that reduce energy requirements during water treatment as well as water demands for energy production.1,2Microbial desalination cells (MDCs)recentlyemerged as a promising technology to simultaneously treat wastewater,desalinate saline water,and produce renewable energy such as electricity or hydrogen gas.3–10MDCs share the same principle of bioelectrochemical reactions with microbial fuel cells (MFCs):electrochemically active bacteria in the anode chamber oxidize biodegradable substrates and generate electron flow (i.e.current)to reduce the electron acceptors in the cathode chamber.The additional desalination function can be achieved in an MDC by adding a middle chamber containing saline water and utilizing the anode–cathode potential difference to drive the migration of anions (e.g.,Cl À)to the anode chamber and cations (e.g.,Na +)to the cathode chamber for charge neutrality.3The MDC process carries great potential in desalination systems,aDepartment of Civil Engineering,University of Colorado Denver,Denver,CO 80004,USA.E-mail:zhiyong.ren@;Tel:+1(303)556-5287bCivil and Environmental Engineering,Colorado School of Mines,Golden,CO 80401,USA†Electronic supplementary information (ESI)available:Two additional figures are included.See DOI:10.1039/c2ee21121aDynamic Article Links CEnergy &Environmental ScienceCite this:Energy Environ.Sci.,2012,5,/eesPAPERP u b l i s h e d o n 13 M a r c h 2012. D o w n l o a d e d b y D a l i a n U n i v e r s i t y o f T e c h n o l o g y o n 13/03/2014 10:23:29.View Article Online / Journal Homepage / Table of Contents for this issuebecause it can either be used as a stand-alone process or serve as a pretreatment for conventional desalination processes such as reverse osmosis (RO)to reduce the salt concentration of RO feed and minimize energy consumption and the membrane fouling potential.Current desalination technologies,such as RO and electrodialysis (ED),are energy and capital intensive.Even the most advanced large scale seawater RO units require 3–7kW h m À3for water desalination,while conventional multi-stage flash evaporation requires 68kW h m À3.11In contrast,the MDC system is considered to be an energy gaining process because it converts the biochemical energy stored in wastewater to elec-tricity or hydrogen b scale MDC studies showed that 180–231%more energy can be recovered as H 2than the reactor energy input when desalinating 5–20g L À1NaCl solutions,4,6and a recent study calculated that a litre-scale MDC can produce up to 58%of the electrical energy required by downstream RO systems.8Current MDC systems use an anion exchange membrane (AEM)to separate the anode and middle chamber,and a cation exchange membrane (CEM)to separate the cathode and middle chamber.Similar to electrodialysis,desalination in MDC is achieved by direct transport of salts from the middle chamber to the anode and cathode chamber.This system faces two main problems.While salts get removed from the middle chamber,they become concentrated in the anode and cathode chambers,resulting in an increase of the volume of saline water.This concern becomes more imperative when wastewater is treated as the anolyte.Although the addition of ions (or total dissolved solids,TDS)increases wastewater conductivity and benefits electricity generation,the increased salinity may affect effluent water quality and prevent subsequent beneficial use of treated wastewater.12,13The high salinity may also affect wastewater treatment efficiency in MDCs because studies showed that high chloride concentration is inhibitory to biological treatment,especially for nutrient removal.14In addition,the AEM between the anode and middle chamber inhibits the free transfer of H +accumulated in the anolyte to other chambers,which causes a significant pH drop in the anode chamber and pH increase in the cathode chamber.15,16A previous study showed that the pH of the wastewater anolyte dropped to 4.2in one batch cycle if no buffer was added to the anolyte.6Additionally,the catholyte pH could increase to 11–13due to the loss of H +.6,16This pH fluc-tuation significantly inhibits bioelectrochemical reaction effi-ciency and reduces system performance.In order to modulate the movement of salts to the anode and cathode chambers,the concept of capacitive deionization (CDI)17was incorporated in this study to develop a sustainable desalination system called a microbial capacitive desalination cell (MCDC).In the proof-of-concept MCDC,salt water can be deionized through electrochemical ion adsorption driven by the electrical field generated by microorganisms.Two activated carbon cloth (ACC)membrane assemblies were designed to connect with the anode and cathode and adsorb ions from water.During desalination,the ions are stored in the electrical double layer capacitors between the solution and the ACC assembly interfaces,thus preventing the salinity increase in treated wastewater.After the ACC is saturated with adsorbed ions,the assembly can be regenerated by removing the electrical potential and the retained salts can be fully recovered in situ for disposal orfurther salt recovery.Another innovative aspect of the MCDC,as compared to conventional MDC,is the use of a second CEM in lieu of AEM between the anode and desalination chamber (Fig.1).This configuration allows cations and protons to move freely from the anode chamber throughout the reactor and therefore maintains electrochemical neutrality and prevents pH fluctuation.In this study,the proof-of-concept MCDC devel-opment and operation are demonstrated,and its advantages over current systems and application potentials are discussed.Materials and methodsMCDC reactor designThe MCDC reactors consisted of three polycarbonate cube-shaped blocks with 3cm diameter holes forming an internal anode,cathode,and desalination chamber volume of 23mL,27mL,and 10mL respectively.The anode and cathode cham-bers had a length of 4cm,while the desalination chamber had a length of 1.5cm.The anode electrode was a graphite brush (Golden Brush,CA)and was pretreated by washing in acetone and heating to 350 C for 30minutes.18Traditional air-cathodes were made by applying 10%Pt/C (0.5mg cm À2)and four PTFE diffusion layers on 30%wet-proofed carbon cloth as previously described.19The desalination chamber was separated from the anode and cathode chamber by two assemblies.Each assembly was constructed by placing a cation exchange membrane (CMX-SB,Astom Corporation,Japan),a Ni/Cu mesh current collector (McMaster Carr,IL),and 3layers of Zorflex Òactivated carbon cloth (ACC,Chemviron Carbon,UK)together.Additionally,the CEM faced the anode/cathode chamber to prevent microbial growth on the assembly.The total weight of the ACC was 1gram with the specific surface area of 1019.8m 2g À1,determined by the Brunauer–Emmet–Teller (BET)method (ASAP 2020,Micro-meritics,Norcross,GA).20The ACC assemblies were connected to the anode/cathode by titanium wires (Fig.1).MCDC operating conditionsTwo reactors were inoculated with anaerobic sludge from the Englewood-Littleton Wastewater Treatment Plant (Englewood,CO)and operated in fed-batch MFC mode.When arepeatableFig.1Diagram of MCDC reactor configuration and operation.Two CEM-ACC assemblies were used to separate the three chambers and capture the removed salts,as well as allow the free transfer of protons.P u b l i s h e d o n 13 M a r c h 2012. D o w n l o a d e d b y D a l i a n U n i v e r s i t y o f T e c h n o l o g y o n 13/03/2014 10:23:29.voltage profile was obtained for consecutive batch cycles,the reactors were shifted to fed-batch MCDC mode by inserting a pair of assemblies and adding one middle chamber as described previously.The anolyte growth media contained per litre:1.6g NaCH 3COO,0.62g NH 4Cl, 4.9g NaH 2PO 4$H 2O,9.2g Na 2HPO 4,0.26g KCl,and 10mL trace metals and 10mL vitamin solution.21The catholyte contained per litre:10g KCl,0.68g KH 2PO 4,0.87g K 2HPO 4.Potassium chloride was used in the cathode chamber to differentiate with sodium transport and monitor the movement of cations from the cathode to the desalination chamber.The salt solution in the desalination chamber contained per litre:10g NaCl,0.49g NaH 2PO 4$H 2O,0.92g Na 2HPO 4.A small amount of buffer was added to the salt solution to some extent mimic the 300to 700m mol kg À1natural buffering capacity of seawater and prevent potential scaling at high pH values.22Two experimental procedures and two controls were per-formed to investigate the desalination performance of the MCDC system.The first experiment investigated simultaneous physical and electrical adsorption capacity by directly adding salt solution into the desalination chamber equipped with ACC assemblies free of adsorbed ion.When the anode and cathode electrodes were connected to the ACC assemblies,physical and electrical adsorption on the ACC assemblies could occur concurrently.The second experimental procedure investigated only electrical adsorption capacity.Electrical adsorption capacity of the ACC assemblies was determined by first adding salt solution to the desalination chamber to allow complete physical adsorption.Electrical adsorption was then characterized by replacing the desalted solution with fresh solution,and con-necting the two assemblies to the anode and cathode,respec-tively.Any residual water from previous experimental washing would have been removed when the salt solution was replaced.Abiotic control experiments were performed by using new brush anodes without bacterial acclimation.The first control experiment measured the physical adsorption capacity by short circuiting the assemblies to ensure no charge was formed across the electrodes.The adsorption capacity of the assemblies was defined as the change in initial and final salt concentration.The second control investigated the electrical adsorption capacity by first allowing complete physical adsorption to occur then by connecting the assemblies to an external power supply at a voltage of 0.53V to simulate the voltage generated by a microbial fuel cell.The MDC control experiment used an anion exchange membrane next to the anode chamber (Astom Corporation,Japan)and a CEM next to the cathode chamber without ACC assemblies in the desalination chamber.An external resistor of 1000Ohms was used between the anode and cathode electrodes,and all other experimental procedures were identical to the MCDC experiments.To regenerate the ACC assemblies in situ for all experiments,the assemblies were either allowed to naturally regenerate or were regenerated by applying an external voltage to increase the rate of regeneration.The natural regeneration was performed by disconnecting the anode and cathode from the assemblies and creating a short circuit between the assemblies with an external wire.Alternatively,an external voltage of 1V in reverse polarity was applied to the assemblies by a programmable power source.The external voltage was applied for 5–10minutes and followedby short circuiting the ACC assemblies,as mentioned above,for 20–30minutes.When the potential difference reached Æ0.5mV,the ACCs were assumed to be regenerated,meaning that any electrically adsorbed ions should have been removed from the electrodes.After regeneration,all electrolytes were emptied and washed with deionized (DI)water to remove any residual salt remaining in the chambers before starting a new batch cycle.Analysis and calculationsConductivity and pH were measured for all three chambers using a conductivity meter and pH meter (HACH Co.,CO).The change in the reactor’s internal resistance was determined through electrochemical impedance spectroscopy (EIS)tests using a Potentiostat.EIS measurements were performed by using the anode as the working electrode,the cathode as the counter electrode,and a saturated Ag/AgCl reference electrode placed in the anode chamber.Results were fitted into equivalent circuit models developed in our previous EIS studies and plotted using Nyquist plots where the ohmic resistance is defined as the intercept of the Zreal axis.21Samples of all three chambers were collected before and after desalination,and after regeneration.Ion concentrations were measured using the Optima 3000Inductive Coupled Plasma (ICP)Spectrometer (Perkin Elmer,CT)and Dionex DC80ion chromatography system (IC)(Dio-nex,CA).Using the data from the IC and ICP,the mass balance of the major ions was determined by summing the concentrations of the ions in each chamber initially,after desalination,and after recovery of the salts.Internal power used was calculated using the following equations:P ¼ðV 2Rd t (1)R ¼r L A(2)where P is power in terms of watt hours,V is the voltage,R is the resistance,r is resistivity,L is length of the resistance,and A is the cross-sectional parisons between the MCDC and CDI were made based off either presented data or estimations from figures in published parison to membrane capacitive deionization (MCDI)was not conducted due to the incompatibility in methodology to the MCDC.Results and discussionReactor desalination performanceDuring MCDC operation,an electrical potential was generated across the microbial anode and air-cathode and applied to the two ACC assemblies to form a double layer capacitor 23–30(Fig.1).The formation of the double layer capacitor has been fully modeled using the Gouy Chapman–Stern theory.29The potential drives the ions to move from the salt solution and adsorb on the activated carbon cloths.The ion adsorption can be observed proportional to the charge formed between the ACC assemblies (Fig.2).Fig.2shows that,in repeated batch cycles,when the potential on the assemblies increases from 0to more than 530mV in each cycle,the solution conductivity in the desalination chamber decreased by 12–18%,from 18mS cm À1toP u b l i s h e d o n 13 M a r c h 2012. D o w n l o a d e d b y D a l i a n U n i v e r s i t y o f T e c h n o l o g y o n 13/03/2014 10:23:29.below 16mS cm À1.The desalination rate was the greatest at the beginning of each cycle and then decreased gradually,suggesting the adsorption capacity of the ACC assemblies decreased along with the increased amount of salt that had been adsorbed in the assemblies.Salt removal was characterized by both conductivity,measured using a conductivity meter,and total dissolved solids (TDS)concentration,measured by IC and ICP (Table 1).Through simultaneous physical and electrical adsorption,the MCDC removed 26.9%of the conductivity or 25.5%of TDS from the desalination chamber in one batch cycle.In addition,a small percentage of salt was removed from the anolyte (4.4%)and catholyte (10.4%)as well.This is likely due to the ions that were driven across the membranes by the electrical potential of the ACC assemblies from the anode and cathode chamber and then adsorbed onto the ACC.Further experiments showed that electrical adsorption alone removed 22.3%TDS from the desa-lination chamber,which contributed up to 88%of the TDS removal compared to the combined physical and electrical adsorption experiments.Table 2compares the normalized TDS removal between the MCDC and CDI studies.The results showed that for the same amount of adsorptive material (ACC),the MCDC improved TDS adsorption by 7–25times.Both MCDC and CDI use an electric field between two electrodes that electrochemically adsorb ions,but the high adsorption from the MCDC may be attributed to the unique feature of the MCDC that uses the internal potential generated by microorganisms.This in situapproach avoided the use of an external power supply and circuit and reduced transportation energy loss,so it demonstrated higher efficiency than traditional CDI processes.The salt adsorption rate in MCDC,however,is lower than published CDI studies,and that is mainly due to the low kinetics of the fed-batch operation and the limited amount of ACC available for ion adsorption.In this study,the MCDC configuration was modified from traditional cubic type MDCs,which only allowed for a total of 1g activated carbon cloth being used in the assembly.This may explain why the amount of salt removed in the desa-lination chamber was relatively small.It was calculated that the amount of salt added in the desalination chamber (114mg TDS)was drastically beyond the control electrical adsorption capacity of the ACC (8.5mg TDS for the 1g ACC applied).Moreover,compared to CDI systems that consume 0.21–1.78Watt hour external energy to generate the potential to remove 1g TDS,the MCDC system does not use any external energy but instead utilized the in situ potential difference between the ACC assem-blies generated during microbial activities.It was calculated that the MCDC reactor saved 2.18Watt hour for 1g of TDS removed.That is why in Table 2the net energy used for the MCDC is negative,indicating that 2.18W h g À1TDS removed was not required,while for the CDI systems an external energy of 0.2–1.78W h is required for removal of 1g TDS.While the MCDC reactor directly uses generated current for desalination,it is possible for electricity to be generated by applying an external load across the ACC assemblies during regeneration.Reactor configuration optimization is underway to increase the ACC loading and further improve desalination efficiency.Sodium,chloride,potassium,and phosphate accounted for greater than 85%of the TDS,and their specific concentration changes in the three chambers are shown in Fig.3.In addition to direct capacitive electrical adsorption that caused concentration decreases in the desalination chamber,a small amount of charged ions migrated from the anode and cathode chamber to the desalination chamber due to the electrical potential or concen-tration gradient.However,the desalination efficiency for the anode and cathode chambers is low compared to the salt removal in the desalination chamber due to the lack of electrical double layer adsorption and the inhibited anion transfer across cation exchange membranes.Results in Table 1showed that saline water can also be used as the catholyte and partially desalinated.Further desalination can be achieved by feeding the treated catholyte to the subsequent reactor’s desalination chamber.The reactor’s internal resistance as measured by EIS at the beginning of the desalination cycle was on average 8.5Ohms.After desalination,the internal resistance increased to anaverageFig.2The correlation between the charge potential across the ACC assemblies and the conductivity changes in the desalination chamber due to electrical adsorption.Arrows indicate changes in electrolyte solution in batch cycles.Table 1Salt removal in terms of conductivity and total dissolved solids in the MCDCPhysical/electrical adsorption Electrical adsorption Desalination chamberAnode chamber Cathode chamber Desalination chamber Anode chamber Cathode chamber %Removal in conductivity 26.9Æ5.113.1Æ3.8 5.6Æ4.410.0Æ0.210.6Æ3.5À2.0Æ2.7%Removal in TDS 25.2Æ3.64.4Æ3.610.4Æ3.622.3Æ3.67.6Æ3.62Æ3.6Total TDS adsorption (mg TDS per g ACC)72.750.7P u b l i s h e d o n 13 M a r c h 2012. D o w n l o a d e d b y D a l i a n U n i v e r s i t y o f T e c h n o l o g y o n 13/03/2014 10:23:29.of 13Ohms (ESI†).The change in conductivity in the desalination chamber correlated closely with the change in internal resistance for the reactor over the course of desalination.The MCDC reactor’s ability to transfer electrons was not inhibited as occurs over the course of desalination in standard MDCs.It is theorized that this is due to the MCDC’s ability to maintain charge neutrality better than MDC reactors.In standard MDC reactors,charge neutrality is reached by ion migrating out of the desali-nation chamber,while in the MCDC reactor charge neutrality is performed by ion migrating through the entire reactor.Assembly regeneration and salt recoveryThe ion saturated ACC assemblies were regenerated using two approaches.The natural regeneration was accomplished by directly connecting the two assemblies in short circuit.The electrical potential across the assemblies was dissipated with the adsorbed salts being released back into solution.When the potential difference across the ACC assemblies reached Æ0.5mV,it was assumed that the ACC assemblies were regen-erated with complete electrical salt desorption.The regeneration rate can be significantly increased by connecting the assemblies to an external power supply of 1V with reverse polarity to facilitate ion desorption (ESI†).Fig.4shows that among the four major ion species,almost all of the electrical adsorbed salts wererecovered during assembly regeneration,shown as a direct correlation between the initial and recovered salt concentrations.The capability of in situ regeneration of the ACC assemblies is another advantage of the MCDC,because the assemblies can be reused many times without investing significantly in materials.Almost all of the adsorbed salts can be recovered in concentrates during regeneration,and the recovered salts can be dewatered or extracted for beneficial uses.Furthermore,MCDC stacks can be developed and integrated with reverse electrodialysis (RED)to capture the energy generated due to the salinity gradient across the concentrate and freshwater.31,32The current MCDC is operated in batch mode,and the desalination and regenerated processes were conducted sequentially.More efficient operation can be achieved by connecting multiple reactors in series or in parallel and operating them in complementary sequential batch reactor (SBR)modes.While some of the units perform desali-nation,others conduct assembly regeneration at the same time.This operation not only provides a continuous flow of produced freshwater but also allows for the direct usage of the electricity produced from regeneration units for desalination units.Reduced pH fluctuationFig.5shows the change in pH units among the three chambers over one typical batch cycle for both the MCDC and the controlTable 2Desalination efficiencies in the MCDC and CDI reactors a Method Electrode materialsElectrodedistance (mm)Net W h/g TDS removed mg TDS/g adsorptive material Reference #MCDC Activated carbon cloth 15À2.1850.74This paper CDI Carbon aerogel2.3+0.217.0017CDI Activated carbon powder NA +1 1.9524CDI Activated carbon powder 0.1+1.78 2.8825CDI Activated carbon powder0.1+1.68 3.1126CDI Activated carbon powder with mesoporous carbon black0.22NA 3.8227CDI MnO 2/nanoporous carbon composite NA NA 0.1028CDI Activated carbon clothNA +0.52NA 30CDIActivated carbon cloth with titaniaNANA4.3834aNA ¼notavailable.Fig.3The concentration changes of the four major ions (potassium,sodium,chloride,phosphate)before and after one typical batch cycle of MCDCoperation.Fig.4Mass balance of the four major ions (potassium,sodium,chlo-ride,phosphate)in the MCDC reactor before and after regeneration.P u b l i s h e d o n 13 M a r c h 2012. D o w n l o a d e d b y D a l i a n U n i v e r s i t y o f T e c h n o l o g y o n 13/03/2014 10:23:29.MDC.The initial pH values in the chambers were all within 7.0Æ0.2.The change in pH for the anode chamber in both the MCDC and the MDC was relatively small,with a drop in pH of between 0.2and 0.5pH units,which was presumably attributed to the high buffering capacity of the anolyte.However,the catholyte had drastically different results between the MCDC and MDC,with the MCDC increasing in pH on average 1.5pH units and the MDC increasing 4.4pH units.Interestingly the change in pH for the desalination chamber for the MCDC is greater than for the MDC control.Previous capacitive deionization studies showed that water electrolysis may cause slight pH variation at low voltages,which may explain the pH increase in the MCDC desalination chamber.30It is difficult to compare the MCDC results directly with CDI studies,because no known CDI experiments have been conducted at a set potential lower than 0.6V.23–30Further investigations should explore the cause of this phenomenon.Because the average percent change between the cathode and desalination chamber were essentially the same,it is assumed that the proton transfer capability of the reactor was not inhibited.The MCDC employs a CEM to separate the anode and desalination chamber.This is different from the AEM used in current MDCs and releases the pH fluctuations in the reactor.In traditional MDCs,anions (Cl À)migrate from the desalination chamber to the anode chamber to compensate for the accumu-lation of H +,but because the AEM prevents the transfer of H +out of the anode chamber,a decrease in pH is observed.By using a CEM,the accumulated H +not only can transfer to the desa-lination chamber but also can transfer further to the cathode chamber and therefore solves the pH change problem in the entire MCDC reactor.Previous studies show that other ions such as Na +and K +also play important roles in maintaining charge balances across different chambers in microbial fuel cells,33but the majority of such ions are adsorbed in the ACC assemblies,so electrolyte charge balance due to ion transfer is not a concern in the MCDC.OutlookThe integration of capacitive deionization with microbial desa-lination provides a sustainable solution that not only addressesthe salt migration and pH fluctuation problems facing current MDC systems,but also improves salt removal and energy effi-ciency compared to CDI systems.Traditional MDCs remove salts from the desalination chamber,but they also add TDS to the anode and cathode chambers and may increase the volume of saline water significantly,depending on different operation configurations.3–10The MCDC reactor demonstrated that desa-lination can be accomplished in the middle chamber without adding salts to the anolyte and catholyte,and therefore released the concerns on the viability of wastewater treatment and reuse due to increased TDS concentration.This proof-of-concept system also demonstrates a microbial desalination reactor to reduce salinity in all three chambers of the reactor.The MCDC system offers a sustainable desalination,renewable energy production,and wastewater treatment.To maximize the benefits and prevent negative effects of salinity changes on the waste-water anolyte,salt migration from the desalination chamber could be modulated by constructing modular plate-shaped ACC-membrane assemblies.If added salt is desired in wastewater to improve the anolyte conductivity,regular MDC operation could be performed.If salt should be prevented from migrating into the anode chamber,the modular ACC assembly plate can be inserted into the reactor to perform salt adsorption.This system inte-gration and operation will provide microbial desalination systems with great flexibility in salt migration management as well as better pH fluctuation control.Despite the potential benefits offered by the MCDC system,many challenges remain to be addressed based on the informa-tion collected from this proof-of-concept study.In addition to the low-cost material development that is required for all bio-electrochemical systems,the adsorptive material can be improved,such as with silica or titanium modification.34,35The reactor configuration needs to be optimized to provide more ACC loading and improve diffusion rate and adsorption capa-bility.Modular stack reactors and flexible operation strategies need to be developed to maximize the integration of desalination and assembly regeneration in multiple units,optimize water recovery,and enhance salt migration management.Improve-ments in MCDCs will also benefit from the continued advances of other bioelectrochemical systems such as microbial fuel cells and capacitive deionization,with the eventual goal of developing a full scale sustainable system directed toward the integration of multiple functions,such as extracting energy from wastewater and water desalination.AcknowledgementsThis work was supported by the Office of Naval Research (ONR)under Awards N0001410M0232.We thank Dr Peter Jenkins for his suggestions and reviewers for their helpful comments.References1M.M.Pendergast and E.M.V.Hoek,Energy Environ.Sci.,2011,4,1946–1971.2J.L.Schnoor,Environ.Sci.Technol.,2011,12,5065.3X.Cao,X.Huang,P.Liang,K.Xiao,Y.Zhou,X.Zhang and B.E.Logan,Environ.Sci.Technol.,2009,43,7148–7152.4M.Mehanna,T.Saito,J.L.Yan,M.Hickner,X.X.Cao,X.Huang and B.E.Logan,Energy Environ.Sci.,2010,3(8),1114–1120.Fig.5The change in pH for the MCDC anode,cathode,and desali-nation chambers compared to the control MDC in one batch cycle.The initial pH values in the chambers of MDC or MCDC all ranged 7.0Æ0.2.P u b l i s h e d o n 13 M a r c h 2012. D o w n l o a d e d b y D a l i a n U n i v e r s i t y o f T e c h n o l o g y o n 13/03/2014 10:23:29.。
PBIL算法求解物流中心选址优化问题
PBIL算法求解物流中心选址优化问题①袁利永1金炳尧2曹振新3(1. 浙江师范大学数理与信息工程学院浙江金华 321004;2.浙江师范大学教师教育学院浙江金华 321004;3.浙江师范大学汽车电气自动化研究中心浙江金华 321004)摘 要: 物流中心的合理布局对整个物流系统的效益有着决定性的影响。
通过对物流中心选址问题相关特点和要求进行研究,我们以建设成本和运行费用最优为目标构造了选址问题的数学模型,设计了基于PBIL的物流中心选址优化算法,并进行了算法的实现和测试。
测试表明,该算法计算速度快、稳定性好,对约束条件增减具有良好的适应性。
最后,提出了该算法的学习概率修正参数动态变化方法,测试表明通过该方法可有效提高算法的收敛速度和寻优能力。
关键字: PBIL; 物流中心; 选址模型; 进化计算; 启发式算法Optimization of Logistics Center Location Using PBIL AlgorithmYUAN Li-Yong1, JIN Bing-Yao2, CAO Zhen-Xing3 (1.College of Information Science and Engineering, ZheJiang Normal University, Jinhua 321004, China; 2.School of Teacher Education, Zhejiang Normal University, Jinhua 321004, China; 3.Research Center of Electrical Automation, Zhejiang Normal University,Jinhua 321004, China)Abstract: Rational distribution of a logistics center has decisive impact on the effectiveness of the entire logistics system. Through research on the characteristics and requirements of logistics center location, we constructeda mathematical model with capital cost and operating costs for the goal of optimal location problem, anddesigned the optimization algorithm of logistics center location based on the PBIL. We also make theimplementation and testing of the algorithm. Tests show that the algorithm has fast speed, good stability, agood adaptability of increasing or decreasing of the constraints. Finally, we have put forward a dynamicchange method of revising parameters of the learning probability in this algorithm, the test shows thatthrough this method can effectively improve the convergence speed and optimization capabilities. Keywords: PBIL; logistics centers; location model; evolutionary computing; heuristic algorithm1引言PBIL是由美国卡内基梅龙大学Baluja,S.提出的进化学习算法[1],它将进化过程视为学习过程,用学习所获取的知识—学习概率来指导产生后代,这种概率是整个进化过程的信息积累,用它指导产生的后代将会更优生(比起GA的双亲基因重组及EP、ES的单个父代变异Gaussian),因而能在许多应用问题中获得更快的收敛速度及更优的结果[2]。
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IEEE/CIC ICCC 2014 Symposium on Wireless Communications SystemsOptimization of Downtilts Adjustment Combining Joint Transmission and 3D Beamforming in 3D MIMOZehua Wei, Ying Wang, Wenxuan LinWireless Technology Innovation Institute, State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications, Beijing 100876, P.R. China Email: opfan21@Abstract—This paper mainly proposes a downtilts adjustment scheme combining joint transmission (JT) and 3-dimension (3D) beamforming in 3D multiple-input multiple-output (MIMO) communication systems, under the new typical scenario in the 3rd generation partnership project (3GPP) which is defined as urban macro cell with one high-rise per sector (3D UMa-H). In our proposed scheme, the specific downtilts adjustment for cellcenter users and cell-edge users are realized through dynamic vertical beamforming. Moreover, the cell-center users are served by 3D beamforming, while the cell-edge users are served by JT and 3D beamforming, which facilitate to maximize both cell-center users’ and cell-edge users’ throughput. To achieve accurate performance evaluation, 3D spatial channel model (3D SCM) and 2-dimension (2D) planar antenna array structure are also considered in our system-level simulation. Simulation results show that our proposed scheme can achieve about 35% gain on cell average spectral efficiency and 42% gain on cell edge user spectral efficiency compared to traditional 2D MIMO without JT.I. I NTRODUCTION Joint transmission (JT), which is one type of coordinated multipoint (CoMP) transmission and reception techniques in Long Term Evolution Advanced (LTE-A) system, has been proved to bring significant gains to system performance since it can mitigate the inter-cell interference (ICI) to increase the received power especially for cell-edge users [1]. However, traditional 2-dimension (2D) beamforming based CoMP techniques limit the received signal strength for the reason that 2D beamforming only utilizes the horizontal plane, with the vertical downtilt being fixed. Another point we should consider is that users are distributed not only in the XY-plane, but also in 3-dimension (3D) axes, especially with the development of economy, the number of high-rises are increasing in metropolis where most of the cell users are located. Therefore, the traditional 2D multiple-input multiple-output (MIMO) is not applicable to the future wireless communication systems which are expected to provide higher data rates. 3D MIMO has been regarded as an important technique to improve vertical coverage as well as overall system capacity [2] [3]. For further improvement of the system performance, beam pattern adaptation should be exploited in the vertical dimension to take advantage of the additional degree of freedom for interference avoidance among adjacent cells, and this is also referred as 3D beamforming [4]. Thesignificant impact of antenna downtilt with 3D beamforming on the performance has been illustrated in [5]. [6] proposes a scheme combining 3D beamforming and JT, but accurate 3D MIMO channel model is not considered. Besides, it does not guarantee the spectral efficiency improvement of both the cell-center and cell-edge users. On the other hand, [7] only evaluates the performance of single cell with different antenna configuration, although it investigates the 3D channel model. What’s more, the aforementioned studies do not consider the situation that cell users locate in 3D axes, especially the highrise case, which is an important character and defined as a typical scenario in 3D MIMO communication systems. In this paper, we propose a downtilts adjustment scheme combining JT and 3D beamforming to achieve the objective of maximizing the cell-center and cell-edge users’ throughput under the new typical scenario of urban macro cell with one high-rise per sector (3D UMa-H) [8]. In our scheme, users at the cell center are served by 3D beamforming, while those cell-edge users are served by JT and 3D beamforming. To get the accurate system-level simulation results, 3D spatial channel model (SCM) and 2D planar antenna array structure are adopted in this paper, which are the bases of 3D MIMO communication systems as well as the realization of 3D beamforming. Simulation results show that our proposed scheme of downtilts adjustment combining JT and 3D beamforming could achieve better performance both on cell average spectral efficiency and cell edge user spectral efficiency. The rest of this paper is organized as follows: Section II introduces the system model, taking UMa cell with one high-rise per sector into consideration. In Section III, we investigate the 3D SCM model, antenna modeling and then our scheme is proposed. Simulation results are shown in Section IV. Conclusion is drawn at last in Section V. II. S YSTEM M ODEL AND P ROBLEM F ORMULATION We consider a downlink orthogonal frequency division multiple access (OFDMA) MIMO system with M hexagonal grid base stations, each deployed with S = 3 cells(sectors). And multiple clusters are deployed in the network layout with the rth cluster’ cells denoted as Cr . Denote {Ks } as the set of users assigned to cell s, and {K } = {K1 }∪{K2 }... ∪{K3M }. We further divide the users in each cell s into cell-center978-1-4799-4146-9/14/$31.00 ©2014 IEEE728IEEE/CIC ICCC 2014 Symposium on Wireless Communications Systemsuser set {Ks,c } and cell-edge user set {Ks,e }. The cell-center users are only served by its serving cell, and the cell-edge users are served by all the cells Cr of the corresponding cluster r [9]. We consider the full frequency reuse, and while adjacent subcarriers have similar fading characteristics, the overall frequency resources are then divided into N orthogonal narrow subbands, corresponding to the resource blocks (RBs). We further consider two downtilts, cell-center user and celledge user specific downtilts, employed in each cell [10]. What’s more, we focus on the new typical scenario of 3D UMa-H, and part of the users in a certain cell are located in the high-rise. We further assume that the high-rise is located at the edge of the cell because this situation becomes common in metropolitan area such as central business district.Aiming to maximize the total throughput, the objective function can be formulated as maxαs s∈Cr(ωks,c Rs,ks,c (αs,1 ) + ωks,e Rs,ks,e (αs,2 )), C1 :n∈N(4)s.t.pn s αs,2Ps , pn s αe , α e0, ∀s ∈ Cr , αs,1 α c , ∀s ∈ C r ,C2 : 0where ωks,c , ωks,e is the weight factor used for allocating RB n to user k . C 1 and C 2 indicate the per-BS power, cellcenter user and cell-edge user specific downtilt constraints, respectively. III. P ROPOSED SCHEME OF DOWNTILTS ADJUSTMENT COMBINING JT AND 3D BEAMFORMING A. 3D SCM Different from traditional 2D SCM, besides the azimuth angle in XY-plane, the 3D SCM needs to adopt the elevation angle with respect to the Z axis as well [8] [11]. A coordinate system is defined by the x, y, z axis, the spherical angles and the spherical unit vectors as shown in Figure 2(a). Figure 2(a) defines the azimuth angle φ and the zenith angle θ in a Cartesian coordinate system, where n ˆ is the given direction, ˆ and φ ˆ are the spherical basis vectors [8]. θ According to the coordinate system, the 3D MIMO channel coefficients for each cluster n and each receiver and transmitter element pair u are generated by Eq. (5), Hu,s,n (t)MDD D ENϵϬΣH&HOO +LJKULVH&HOO Fig. 1.3D MIMO system model under investiagtionThe system model is illustrated in Fig. 1. Take cell 1 for example, denote α1 and α2 as the cell-center user and celledge user specific downtilts in a certain cell respectively, βke is the cell-edge user’ vertical angle according to the location, where α1 = αs,1 , α2 = αs,2 , βke = βks,e , and αs = (αs,1 , αs,2 ) for the s-th cell. The vertical angle between the desired signal radiated towards a cell-edge user ks,e and the serving beamforming is αs,2 − βks,e . Therefore, the desired signal strength of cell-edge user should be written n n as pn s Hks,e cos(αs,2 − βks,e ), where ps denotes the allocated n power for RB n in s-th cell, Hks,e represents the channel gain of user k served by the s-th cell on RB n. Hence the signal to interference plus noise ratio (SINR) of the cell-edge user in JT mode can be given by SIN Rks,e (αs,2 ) =s∈Cr n pn s Hks,e cos(αs,2 − βks,e )= × ×Pn /Mm=1Frx,u,θ (θn,m,ZOA , ϕn,m,AOA ) Frx,u,ϕ (θn,m,ZOA , ϕn,m,AOA )Texp j Φθθ n,m κn,m −1 exp j Φϕθ n,mκn,m −1 exp j Φθϕ n,m exp j Φϕϕ n,mFtx,s,θ (θn,m,ZOD , ϕn,m,AOD ) Ftx,s,ϕ (θn,m,ZOD , ϕn,m,AOD )1 T ¯ r ˆrx,n,m × exp j 2πλ− .d 0 rx,uσ2,(1)and that of the cell-center user can be expressed as SIN Rks,c (αs,1 ) =n pn s Hks,c cos(αs,1 − βks,c )σ2 +(2) Thus, according to Shannon formula, we can get the achievable data rate for user k on RB n in s-th cell which is approximated as Rs,ks (αs ) = log2 [1 + SIN Rks (αs )] , (3)j ∈Cr ,j =sn pn j Hkj,c cos(αj,1 − βkj,c ),(5) where Frx,u,θ and Frx,u,φ denote the receive antenna element u field patterns in the direction of the spherical basis ˆ and φ ˆ respectively. Ftx,s,θ and Ftx,s,φ are the vectors, θ transmit antenna element s field patterns in the direction of ˆ and φ ˆ respectively. The symbols the spherical basis vectors, θ θn,m,ZOA , θn,m,ZOD , φn,m,AOA and φn,m,AOD are the zenith angle of arrival (ZOA), the zenith angle of departure (ZOD), the azimuth angle of arrival (AOA), and the azimuth angle of departure (AOD) in the ray m of cluster n, respectively. r ˆrx,n,m is the spherical unit vector with θn,m,ZOA and φn,m,AOA , r ˆtx,n,m is the spherical unit vector with θn,m,ZOD ¯rx,u , d ¯tx,u are the location of receive antenna and φn,m,AOD . d element u and transmit antenna element s respectively, and1 T ¯tx,s × exp j 2πλ− .d r ˆtx,n,m 0exp (j 2πvn,m t) ,729IEEE/CIC ICCC 2014 Symposium on Wireless Communications Systems¯rx,u and d ¯tx,u are discussed in next the modeling details of d subsection. vn,m denotes the doppler frequency shift which is calculated from AOA, ZOA and the user’ velocity vector.]ĂĂ//ĂĂ/&ĂĂTQ ЦЦ IĂĂ ĂĂTЦI\&[。