An improvement on “Integrated production strategy and reuse scenario: A CoFAQ model and case study of mail server system development”

The authors (Tang et al. (2013) [1] developed a CoFAQ model to formulate a solution for the problem of production strategy decision and reuse scenario selection for a software product family. In the previous research, we stated that the CoFAQ model was a 0–1 mixed integer nonlinear program, where only a local optimal solution might be found. In a recent study, we found that the CoFAQ could be transformed into a 0–1 mixed integer linear programming model. By solving the model, a global optimal solution can be obtained. In this paper, we present the improved formulation and the optimal solution for the case study.     

基于协同效应的知识创新团队伙伴选择方法

在知识创新团队的伙伴选择问题中着重考虑了伙伴间的协同效应信息。首先,分析了伙伴之间的协同关系与协同效应,描述了考虑多个协同效应评价指标的知识创新团队伙伴选择问题;然后,建立了团队伙伴选择的数学模型,该模型是一个0-1二次整数规划问题,为了求解该问题,开发了一种GRASP启发式算法;最后,通过一个实例分析说明了所提出方法的可行性和实际应用价值。     

动态联盟中伙伴选择问题的区间规划模型及其求解

伙伴选择和风险管理是动态联盟中的重要决策问题,当考虑失败风险时,失败概率无法给出精确值,因此,考虑采用不确定性规划描述此类问题.提出动态联盟中伙伴选择问题的区间规划模型,模型中用区间数表示联盟伙伴的失败概率.为了求解该模型,引入序关系,并利用Nakahara和Ishibuchi的定理,将区间规划模型转化为等价的清晰双目标模型.设计带自适应适值函数的遗传算法,求出问题的全部非劣解.经过对多个问题的仿真,证明了算法的有效性.     

A BRANCH AND BOUND PROCEDURE FOR SETUP PROBLEMS IN FLEXIBLE MANUFACTURING SYSTEMS

We address a medium- to short-term production planning problem in a flexible manufacturing environment. First we present a single-machine, mixed integer programming model for part type selection and lot-sizing problems over a T-period planning horizon. Demand for part types changes dynamically through the periods. The objective is to meet the demand for part types during the periods they are demanded. Available machine time and tool magazine capacities are the system constraints in our models. We next extend on the single- machine model to include multiple machines. In addition to part type selection and lotsizing decisions, the extended model also addresses the machine-loading decision. We present exact branch and bound procedures based on linear programming relaxations for the two models. We also report the results of our computational experiments.     

PORTFOLIO MODELING IN MULTIPLE-CRITERIA SITUATIONS UNDER UNCERTAINTY

There have been many models for portfolio selection, but most do not explicitly include uncertainty and multiple objectives. This paper presents an approach that includes these aspects using a form of stochastic integer programming with recourse. The method involves the use of a time-based decision tree structure called a “project tree.” Using this basic format, an illustrative six-project example is presented and analyzed. Various forms of objectives are discussed, ranging from the maximization of expected portfolio value to the maximization of the minimum weighted portfolio deviation from two goals. In each case, formulated numerical problems are given, and the solutions derived are presented. The approach is shown to be very flexible and capable of handling a variety of situations and objectives.     

A fuzzy set approach for R&D portfolio selection using a real options valuation model

Making R&D portfolio decision is difficult, because long lead times of R&D and market and technology dynamics lead to unavailable and unreliable collected data for portfolio management. The objective of this research is to develop a fuzzy R&D portfolio selection model to hedge against the R&D uncertainty. Fuzzy set theory is applied to model uncertain and flexible project information. Since traditional project valuation methods often underestimate the risky project, a fuzzy compound-options model is used to evaluate the value of each R&D project. The R&D portfolio selection problem is formulated as a fuzzy zero–one integer programming model that can handle both uncertain and flexible parameters to determine the optimal project portfolio. A new transformation method based on qualitative possibility theory is developed to convert the fuzzy portfolio selection model into a crisp mathematical model from the risk-averse perspective. The transformed model can be solved by an optimization technique. An example is used to illustrate the proposed approach. We conclude that the proposed approach can assist decision makers in selecting suitable R&D portfolios, while there is a lack of reliable project information.     

Optimizing real-time vehicle sequencing of a paint shop conveyor system

A discrete event simulation model and a decision optimizer that were developed for a General Motors paint shop conveyor system are presented. The simulation model interacts with the decision optimizer at four critical points in the system, trying to regroup batches of different colored vehicles. The decision optimizer employs dynamic programming and integer programming to optimize vehicle routing policies. Simulation results of the current decision making policies are compared with those of the proposed optimized policies showing that the number of paint head changes can be significantly reduced resulting in substantial savings on paint head cleaners and paint.     

Reconfiguring the Supply Network Using Current Performance Data

Reconfiguration of the supply chain network from time to time is essential for businesses to retain their competitive edge. This paper presents a methodology for reconfiguration of an existing supply chain network. The methodology is characterized by two decision levels. In the first level, the current network performance is evaluated and efficient practices are identified. In the next level, a model that incorporates efficient practices is developed to reconfigure the network. This integrated methodology allows for decision maker (DM) input throughout the process. The methodology has been implemented and tested in the reconfiguration of an outbound petroleum supply chain network for CountryMark Cooperative, Inc. In this case study, Data Envelopment Analysis (DEA) is used to analyze current operations and an integer programming (IP) model that incorporates efficiency metrics is developed for selection of distribution facilities and allocation of resources to the facilities. Use of this methodology can lead to improved operations and reduced operating expenses.     

A Strategic,Area-wide Contingency Planning Model for Oil Spill Cleanup Operations with Application Demonstrated to the Galveston Bay Area*

This paper develops a general integer programming model for strategic, area-wide contingency planning of oil spill cleanup operations. Model inputs include the set of risk points and the likely spill scenarios and response requirements for each, the sites of existing storage locations and the inventory of components at each, and potential sites for new storage locations. The model prescribes a minimum total cost plan to either build new storage locations, expand existing ones, or both, to purchase new components and pre-position them, and a contingency plan that determines which response systems should be composed to enable an effective time-phased response for each likely spill scenario. A family of heuristics based on linear programming (LP) is devised to resolve this strategic problem, providing an area-wide contingency plan. The heuristics are evaluated on a set of 10 test problems that involve 1869 general integer variables and 3264 constraints. Computational tests indicate that four heuristics are quite effective, prescribing solutions for each of 10 test cases within 1.41% of optimum and within a few minutes runtime. This study focused on modeling the Galveston Bay Area, and the test problems represent application in that area. A sensitivity analysis is demonstrated by assessing the impacts of component availability and the degradation of cleanup capability over time. Use of the model as a decision support aid by responsible parties, contractors, governmental organizations and others is described.     

Responsive contingency planning in supply risk management by considering congestion effects

Contingency rerouting is known as a cost-effective risk management strategy for major disruptions such as earthquakes and natural disasters. The objective of this paper is to develop a decision-making tool to determine the appropriate response speed of a volume-flexible backup supplier to improve the supply chain responsiveness. We propose a mixed integer programming (MIP)-based capacity planning tool which generates the contingency plan of the supply chain subject to random disruptions. In order to make an accurate decision, the impact of critical operational characteristics such as response time and congestion are considered in a disruption scenario. The appropriate response speed is selected through a decision tree analysis by minimizing the expected supply chain costs. The selection is made with respect to three different attitudes of the decision maker towards risk. In order to evaluate the impact of the different failure and recovery probabilities over the selection process, a sensitivity analysis is presented. The results show that considering congestion is especially critical for risk-neutral decision makers in mitigating against disruptions.     

SCHEDULING PARALLEL PROCESSORS WITH SETUP COSTS AND RESOURCE LIMITATIONS

An integer linear programming model is presented for the scheduling of n products on m identical machines. The particular problem studied is one that occurs frequently in the fiberglass and textile industries. The model incorporates setup costs, lost production costs, and overtime costs. Due to the structure of the model, integer solutions can be obtained by explicitly restricting only a small number of the integer variables. This allows those responsible for scheduling to solve realistically sized problems in an efficient manner. Computational results are provided for a set of generated test problems.     

基于持续运营机会约束的竞争设施点选址研究——一种有效的实数编码遗传求解算法

竞争设施点选址是空间经济、区域发展、组合优化和系统工程的重要课题之一。本文以市场份额最大化为目标,研究了基于持续运营机会约束的竞争设施点选址问题,并给出了一种有效的实数编码遗传求解算法。在求解模型方面,首先假定运营成本是竞争设施点规模大小的函数,并对设施点持续运营概率进行机会约束,借鉴引力模型建立竞争设施点选址-设计问题的非线性混合整数规划模型。其次,考虑到选址变量和规模变量的数值类型,以及编码变换问题,设计了一种实数编码遗传求解算法。通过数值实验表明,对不同规模问题的实际计算结果,该算法可以在较短时间内获得最优解,可行解和精确解之间误差小于0.5%,相关比较分析也讨论了该算法的优越性和实用性,为竞争设施点选址问题的研究提供了不同的视角和实用求解算法。     

An application of multiple objective linear programming to media selection

A multiple objective linear programming (MOLP) method utilizing interval criterion weights is applied to the problem of media selection. Using this technique, it is possible to analyze a problem more explicitly in terms of the several objectives inherent in many media selection situations. In order to illustrate the interval criterion weights approach, a multiple objective hierarchical media selection model is presented and its computer results discussed. In addition to being able to deal more directly with different decision criteria, a distinguishing feature of the mathematical analysis applied here is that its output enables the media-planner to be presented with a small cluster of candidate media schedules (rather than just one). Then, from this list, the media-planner should be in a position to qualitatively make a final choice as a close approximation to his optimal solution.     

考虑不同碳排放处理模式的动态供应商选择及采购批量问题研究

不同的碳排放处理模式及不确定的市场需求等因素影响下,如何选择供应商并确定采购批量直接影响企业的运营和效益。本文在多时间周期、多产品种类、多供应商及随机需求情形下,同时考虑不同碳排放处理模式,分析动态供应商选择及采购批量等最优决策问题,构建混合整数非线性规划模型。通过设计变异算子和扰动因子来改进粒子群算法,力求在短时间内求解大规模决策问题。针对不同规模供应商选择及采购批量决策问题,采用精确方法、近似方法和改进粒子群算法求解。数值实验验证了模型及改进粒子群算法的有效性和可行性,分析了碳税、碳交易价格及碳限额对供应链管理的影响,并给出了供应商选择及碳排放处理的决策参考建议。     

A MATHEMATICAL PROGRAMMING MODEL FOR FACULTY COURSE ASSIGNMENTS

This paper describes an integer programming model that we believe has advantages over other models for determining faculty teaching assignments. Unlike previously proposed models, in this model the decision variables represent the assignment of complete teaching schedules rather than courses to faculty members. The paper compares the proposed model with previous models for application in an academic department.     

基于实物期权的项目组合投资决策研究

基于项目投资决策的可延迟性特征,将实物期权的决策灵活性思想引入到企业的投资组合决策中,建立了基于实物期权的0-1整数规划模型.模型以项目的期权价值最大化作为投资组合项目选择的标准,通过项目组合投资时机的灵活安排,实现了项目组合的总投资价值最大化.     

基于期权视角的海外市场拓展的双目标多项目多期优化随机机会约束模型

随着经济全球化趋势进程的加快,海外市场资源优化配置逐步成为理论研究和实务关注的热点,而已有研究却鲜有涉及海外目标市场开拓的资源配置问题。本文在对此问题进行分析的基础上,提出了基于期权度量的收益目标模型和基于信息熵度量的风险目标模型,构建了随机环境下基于现金流供需约束的双目标多项目多期优化0-1机会约束优化模型。在吸取NSGA-Ⅱ算法思想的基础上对DE算法进行了改进,设计了求解此类配置问题的Pareto解算法,比较了伸缩因子分别为固定数和随机数时差分算法的性能,得出后者算法性能稍微优于前者。     

An integrated production and inventory model for a whole manufacturing supply chain involving reverse logistics with finite horizon period

This paper proposes a model and solution method for coordinating integrated production and inventory cycles in a whole manufacturing supply chain involving reverse logistics for multiple items with finite horizon period. A whole manufacturing supply chain involving reverse logistic consists of tier-2 suppliers supplying raw materials to tier-1 suppliers, tier-1 suppliers producing parts, a manufacturer which manufactures and assembles parts from tier-1 suppliers into finished products, distributors distributing finished products to retailers, retailers selling products to end customers and a third party which collects the used finished products from end customers, dissembles collected products into parts, and feed the parts back to the supply chain. In this system, we consider a finite horizon period. A mathematical model for representing the behaviors of the system is developed. Solution methods based on decentralized and a combination of decentralized and centralized decision making process, referred to as the semi-centralized decision making process, are proposed to solve the model while the centralized decision making process is solved by a mixed integer nonlinear programming method. A numerical example is used to demonstrate the model and the solutions based on the three types of the coordination.     

Coordinated Contract Decisions in a Make‐to‐Order Manufacturing Supply Chain: A Stochastic Programming Approach

One of the important objectives of supply chain S&OP (Sales and Operations Planning) is the profitable alignment of customer demand with supply chain capabilities through the coordinated planning of sales, production, distribution, and procurement. In the make‐to‐order manufacturing context considered in this paper, sales plans cover both contract and spot sales, and procurement plans require the selection of supplier contracts. S&OP decisions also involve the allocation of capacity to support sales plans. This article studies the coordinated contract selection and capacity allocation problem, in a three‐tier manufacturing supply chain, with the objective to maximize the manufacturer's profitability. Using a modeling approach based on stochastic programming with recourse, we show how these S&OP decisions can be made taking into account economic, market, supply, and system uncertainties. The research is based on a real business case in the Oriented Strand Board (OSB) industry. The computational results show that the proposed approach provides realistic and robust solutions. For the case considered, the planning method elaborated yields significant performance improvements over the solutions obtained from the mixed integer programming model previously suggested for S&OP.     

Column generation with free replicability in DEA

The evaluation of efficiency scores in data envelopment analysis is based on the construction of artificial decision making units subject to some assumptions, usually requiring convexity of the production possibility set. This demands divisibility in input and output, which is not always possible. The so-called free replicability model, proposed by Henry Tulkens, permits input and output to enter in only discrete amounts. The model is of a mixed integer programming type, for which the number of variables, here corresponding to the decision making units, may be critical in order to reach an optimal solution.