Performance Measures for Selection of Metamodels to be Used in Simulation Optimization

This paper points out the need for performance measures in the context of simulation optimization and suggests six such measures. Two of the measures are indications of absolute performance, whereas the other four are useful in assessing the relative performance of various candidate metamodels. The measures assess performance on three fronts: accuracy of placing optima in the correct location, fit to the response, and fit to the character of the surface (expressed in terms of the number of optima). Examples are given providing evidence of the measures' utility—one in a limited scenario deciding which of two competing metamodels to use as simulation optimization response surfaces vary, and the other in a scenario of a researcher developing a new, sequential optimization search procedure.     

Optimal design of supply chain networks under uncertain transient demand variations

We consider a detailed mathematical formulation for the problem of designing supply chain networks comprising multiproduct production facilities with shared production resources, warehouses, distribution centers and customer zones and operating under time varying demand uncertainty. Uncertainty is captured in terms of a number of likely scenarios possible to materialize during the lifetime of the network. The problem is formulated as a mixed-integer linear programming problem and solved to global optimality using standard branch-and-bound techniques. A case study concerned with the establishment of Europe-wide supply chain is used to illustrate the applicability and efficiency of the proposed approach. The results obtained provide a good indication of the value of having a model that takes into account the complex interactions that exist in such networks and the effect of inventory levels to the design and operation.     

Capacity rationing in rental systems with two customer classes and batch arrivals

Service differentiation is an emerging method to improve profit and to better serve high-priority customers. Such an approach has recently been introduced by one of Europe's leading rail cargo companies. Under this approach, customers can choose between classic and premium services. Premium service is priced above classic service and premium customers receive a service guarantee which classic customers do not receive. The company has to decide under which conditions it should ration its fleet capacity to classic customers in order to increase service of premium customers. We model such a situation as a batch-arrival queuing loss system. We describe the model, solve it optimally, and derive quantities of interest such as service probabilities. We further analyze it by performing numerical experiments based on the data from the company that motivated our research. We show that the potential of capacity rationing can be substantial in situations like the one we analyzed. We also derive conditions under which rationing is especially beneficial, such as under high unit fleet holding costs or in the presence of batch arrivals compared to single arrivals.     

Models and algorithms for the design of survivable multicommodity flow networks with general failure scenarios

We consider the design of a multicommodity flow network, in which point-to-point demands are routed across the network subject to link capacity restrictions. Such a design must build enough capacity and diverse routing paths through the network to ensure that feasible multicommodity flows continue to exist, even when components of the network fail. In this paper, we examine several methodologies to optimally design a minimum-cost survivable network that continues to support a multicommodity flow under any of a given set of failure scenarios, where each failure scenario consists of the simultaneous failure of multiple arcs. We begin by providing a single extensive form mixed-integer programming formulation for this problem, along with a Benders decomposition algorithm as an alternative to the extensive form approach. We next investigate strategies to improve the performance of the algorithm by augmenting the master problem with several valid inequalities such as cover constraints, connectivity constraints, and path constraints. For the smallest instances (eight nodes, 10 origin–destination pairs, and 10 failure scenarios), the Benders implementation consumes only 10% of the time required by the mixed-integer programming formulation, and our best augmentation strategy reduces the solution time by another 50%. For medium- and large-sized instances, the extensive form problem fails to terminate within 2 h on any instance, while our decomposition algorithms provide optimal solutions on all but two problem instances.     

财务管理目标与企业资本结构优化

讨论了财务管理最优目标、企业价值最大化、资本结构优化的关系,分析了资本结构优化应考虑的基本原则和主要因素。     

A genetic algorithm-based decomposition approach to solve an integrated equipment-workforce-service planning problem

We develop a new genetic algorithm to solve an integrated Equipment-Workforce-Service Planning problem, which features extremely large scales and complex constraints. Compared with the canonical genetic algorithm, the new algorithm is innovative in four respects: (1) The new algorithm addresses epistasis of genes by decomposing the problem variables into evolutionary variables, which evolve with the genetic operators, and the optimization variables, which are derived by solving corresponding optimization problems. (2) The new algorithm introduces the concept of Capacity Threshold and calculates the Set of Efficient and Valid Equipment Assignments to preclude unpromising solution spaces, which allows the algorithm to search much narrowed but promising solution spaces in a more efficient way. (3) The new algorithm modifies the traditional genetic crossover and mutation operators to incorporate the gene dependency in the evolutionary procedure. (4) The new algorithm proposes a new genetic operator, self-evolution, to simulate the growth procedure of an individual in nature and use it for guided improvements of individuals. The new genetic algorithm design is proven very effective and robust in various numerical tests, compared to the integer programming algorithm and the canonical genetic algorithm. When the integer programming algorithm is unable to solve the large-scale problem instances or cannot provide good solutions in acceptable times, and the canonical genetic algorithm is incapable of handling the complex constraints of these instances, the new genetic algorithm obtains the optimal or close-to-optimal solutions within seconds for instances as large as 84 million integer variables and 82 thousand constraints.     

农业结构优化对土地生产率和农业增长的影响

采用土地生产率分解法,将农业增长的决定因素分解为导致土地生产率增长的非结构优化因素和结构优化因素,利用中国1979-2006年土地生产率的数据,分别估算其对农业增长的贡献,并进一步分析影响农业结构优化对农业增长贡献相关因素,发现农户对农产品价格预期的准确性对其影响最为显著。     

陕西地市本科高校专业设置的现状及优化策略

陕西地市本科高校专业设置的优化有利于地市本科高校与地方经济社会发展的有效对接,实现地市本科高校与地方经济社会发展的＂共赢＂。针对地市本科高校专业设置的特点与存在的问题,应以认识论与政治论相统一的高等教育哲学观、区域经济社会发展需求和地市本科高校自身的功能定位为依据,探索宽口径专业人才培养模式,适量增设与所在区域经济社会产业结构对接的目录外专业,建立专业设置预警与退出机制,加强对专业设置的宏观调控与管理,优化地市本科高校专业设置结构,构建服务型本科专业体系。     

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.     

Supply Performance in Multi‐Echelon Inventory Systems with Intermediate Product Demand: A Perspective on Allocation

In this article, we study the performance of multi‐echelon inventory systems with intermediate, external product demand in one or more upper echelons. This type of problem is of general interest in inventory theory and of particular importance in supply chain systems with both end‐product demand and spare parts (subassemblies) demand. The multi‐echelon inventory system considered here is a combination of assembly and serial stages with direct demand from more than one node. The aspect of multiple sources of demands leads to interesting inventory allocation problems. The demand and capacity at each node are considered stochastic in nature. A fixed supply and manufacturing lead time is used between the stages. We develop mathematical models for these multi‐echelon systems, which describe the inventory dynamics and allow simulation of the system. A simulation‐based inventory optimization approach is developed to search for the best base‐stock levels for these systems. The gradient estimation technique of perturbation analysis is used to derive sample‐path estimators. We consider four allocation schemes: lexicographic with priority to intermediate demand, lexiographic with priority to downstream demand, predetermined proportional allocation, and proportional allocation. Based on the numerical results we find that no single allocation policy is appropriate under all conditions. Depending on the combinations of variability and utilization we identify conditions under which use of certain allocation polices across the supply chain result in lower costs. Further, we determine how selection of an inappropriate allocation policy in the presence of scarce on‐hand inventory could result in downstream nodes facing acute shortages. Consequently we provide insight on why good allocation policies work well under differing sets of operating conditions.