基于组合赋权TOPSIS模型的物流配送中心选址研究

物流的发展离不开配送中心的建设,配送中心建设的首要问题是选址,通过分析影响物流配送中心选址的各种因素,建立了包括自然条件、经营环境、基础设施、成本因素等因素的配送中心选址指标体系.采用层次分析法(AHP)和熵值法组合确定各指标的权重,依据逼近理想解排序(TOPSIS)法的基本思路,建立物流配送中心选址模型.通过对物流配送中心进行选址的实例研究,证明方法的有效性并选出最优方案.     

数据包络分析中诸C~2GS~2模型的最优解(最优值)的关系的探讨

对三种C2 GS2模型(分别为基于输入、基于输出、基于输入与输出) ,讨论了其最优值的关系,还讨论了三个模型的最优解的关系.     

一种新型劳务众包平台的市场策略与定价研究

定义并研究了自助式空间劳务众包这一新型众包模式,给出相关定义和众包平台的运行规则,提出会员行为仿真算法模拟市场.根据这类劳务众包平台增加利润和扩大市场份额的普遍需求,基于双边市场理论,提出任务打包机制、定价多目标规划模型和抢单顺序多目标规划模型.采用带精英策略的非支配排序的遗传算法(NSGA-Ⅱ)求解问题的帕累托解集(Pareto Front),最后应用逼近理想点法(TOPSIS)根据平台战略目标给出最优解.规划模型在数据集上应用效果良好,任务完成率能在有效控制成本的前提下提升30%至45%,新用户参与率能在保证较高的任务完成率的前提下提升150%至250%.     

考虑应急设施中断风险与防御的可靠性选址模型研究

为提高应急设施运行的可靠性和抵御中断风险的能力, 研究中断情境下的应急设施选址-分配决策问题。扩展传统无容量限制的固定费用选址模型, 从抵御设施中断的视角和提高服务质量的视角建立选址布局网络的双目标优化模型, 以应急设施的建立成本和抵御设施中断的加固成本最小为目标, 以最大化覆盖服务质量水平为目标, 在加固预算有限及最大最小容量限制约束下, 构建中断情境下应急设施的可靠性选址决策优化模型。针对所构建模型的特性利用非支配排序多目标遗传算法(NSGA-Ⅱ)求解该模型, 得到多目标的Pareto前沿解集。以不同的算例分析和验证模型和算法的可行性。在获得Pareto前沿的同时对不同中断概率进行灵敏度分析, 给出Pareto最优解集的分布及应急设施选址布局网络的拓扑结构。     

基于ANP与模糊TOPSIS-CRITIC方法的不确定多属性决策模型

随着多属性决策问题的日益流行,处理决策问题的复杂程度也逐渐增加,针对其中权重不确定,难以量化各影响因素主观权重与客观权重以及指标排序不精确的问题,提出了一种将网络层次分析法(ANP)与模糊指标相关性的指标权重确定法(CRITIC)、逼近理想解排序法(TOPSIS)相结合的不确定多属性决策模型首先,在分别使用ANP方法和...     

IS/IT项目选择决策的灰色逼近理想解排序法

IS/IT项目选择决策是一个多属性决策问题.针对传统逼近理想解排序法(TOPSIS)在确定属性权重系数上的缺陷,并考虑到在实际IS/IT项目选择决策过程中部分决策信息的不足,提出了基于灰色TOPSIS改进算法.算法运用区间灰数表达指标权重和指标评价值,定义备择项目与正、负理想解的灰色关联度,依此计算各备则项目的贴近度并实现最终排序.仿真实例验证了该方法的合理和有效性.     

一种基于DE算法和NSGA-Ⅱ的多目标混合进化算法

设计了一种新颖的基于差分进化算法和NSGA-Ⅱ的混合进化算法用来解决多目标优化问题。在此算法中,根据算法的搜索情况设计相应的自适应变异算子,以便在突变操作中找到Pareto解。同时,选择操作将基于NSGA-Ⅱ快速非优超排序和拥挤机制将父代与子代的双种群进行截短,确保最优解不会丢失并保证解的多样性。三个经典测试函数的仿真结果表明,文中算法在实现多目标优化问题的两个目标(获得收敛于真实Pareto前沿的解和解沿着前沿均匀扩展)方面表现出良好的综合性能。     

随机混流U型拆卸线平衡排序问题多目标进化算法优化

针对混流U型拆卸线平衡排序问题,考虑拆卸时间不确定,建立了该问题最小拆卸线平均闲置率、尽早拆卸危害和高需求零部件、最小化平均方向改变次数的多目标优化模型,并提出一种基于分解和动态邻域搜索的混合多目标进化算法(Hybrid Multi-objective Evolutionary Algorithm Based on Decomposition, HMOEA/D)。该算法通过采用弹性任务分配策略、动态邻域结构和动态调整权重以保证解的可行性并搜索得到分布较好的非劣解集。最后,仿真求解实验设计技术(DOE)生成的测试算例,结果表明HMOEA/D较其它算法能得到更接近Pareto最优、分布更好的近似解集。     

离散时间MDP矩最优模型——折扣依赖于历史的情形

本文在S、A(i)(i∈S)均匀可列集情形下,建立了折扣依赖于历史的矩最优模型。给出了折扣总报酬k阶矩在各类策略下的统一表达式;讨论了矩最优策略的结构与性质;证明了矩最优方程在给定条件下,存在唯一的有界解。     

双目标瓶颈指派问题的遗传算法

给出一种双目标瓶颈指派问题的新模型,本模型结合了决策者和工人两方面的因素,特别之处在于考虑到了工人对工作的排名偏好.进而,将双目标瓶颈指派问题转化为单目标规划,并设计了解此问题的遗传算法,算法的解均为双目标瓶颈指派问题的Pareto最优解.     

Pruned Pareto-optimal sets for the system redundancy allocation problem based on multiple prioritized objectives

In this paper, a new methodology is presented to solve different versions of multi-objective system redundancy allocation problems with prioritized objectives. Multi-objective problems are often solved by modifying them into equivalent single objective problems using pre-defined weights or utility functions. Then, a multi-objective problem is solved similar to a single objective problem returning a single solution. These methods can be problematic because assigning appropriate numerical values (i.e., weights) to an objective function can be challenging for many practitioners. On the other hand, methods such as genetic algorithms and tabu search often yield numerous non-dominated Pareto optimal solutions, which makes the selection of one single best solution very difficult. In this research, a tabu search meta-heuristic approach is used to initially find the entire Pareto-optimal front, and then, Monte-Carlo simulation provides a decision maker with a pruned and prioritized set of Pareto-optimal solutions based on user-defined objective function preferences. The purpose of this study is to create a bridge between Pareto optimality and single solution approaches.     

TOPSIS for bi-level MODM problems

TOPSIS (technique for order preference by similarity to ideal solution) is a multiple criteria method to identify solutions from a finite set of alternatives based upon simultaneous minimization of distance from an ideal point and maximization of distance from a nadir point. This paper proposes a fuzzy TOPSIS algorithm to solve bi-level multi-objective decision-making (BL-MODM) problems, and in which the objective function at each level are non-linear functions which are to be maximized. The proposed model for getting the satisfactory solution of the BL-MODM problems includes the membership functions for the upper level decision variables vector with possible tolerances, the membership function of the distance function from the positive ideal solution (PIS) and the membership function of the distance function from the negative ideal solution (NIS). A numerical illustrative example is given to clarify the proposed TOPSIS approach of this paper.     

基于电能节约的E-DEA模型及其应用

按照全要素能源效率的概念,重点考虑电能投入约束,构造了基于电能节约的E-DEA模型,其目标函数为极大化产出比例和电能投入比例之差,约束条件中除考虑一般投入量约束外,还同时强调电能投入径向节约和产出径向增加。根据模型最优解,给出了相应的有效、非有效、弱有效、用电规模收益状态的判断准则,以及相应于不同有效性情况下决策单元的改进。以合肥市通用制造业规上企业所属21个行业为研究对象,从第二次经济普查中选择年均资产、从业人员、电力、非电力能源、二氧化碳排量为投入指标,主营业务收入为产出指标,对行业电能利用效率进行实证分析,通过分析潜在电能可节约量和主营业务收入可增加量,明确了各行业改进目标。     

Applying inverse DEA and cone constraint to sensitivity analysis of DMUs with undesirable inputs and outputs

In this paper, the inverse data envelopment analysis (DEA) with the preference of cone constraints will be discussed in a way that in the decision-making units, the undesirable inputs and outputs exist simultaneously. Supposing that the efficiency level does not change, if the unit under assessment increases the level of the desirable outputs and decreases the level of the undesirable outputs, how will it affect the amount of the desirable input level and the undesirable input level? To answer this question, the application of the inverse DEA with preference of cone constraints is suggested. The suggested approach, while maintaining the efficiency level, increases the level of its undesirable input and decreases the level of its desirable input by selection of strongly efficient solutions or some weakly efficient solutions of the multiple objective linear programming (MOLP) model. While maintaining the efficiency level, the suggested approach by selection of strongly efficient solution or some of the weakly efficient solutions of the MOLP model can increase the undesirable input level and decrease the desirable input level. Similarly, the suggested approach can be applied if the decision-making unit increases its undesirable input level and decreases the desirable input level so that the undesirable output level decreases and the desirable output level increases while maintaining the efficiency level. As an illustration, two numerical examples are rendered.     

动态投入产出系统的稳定性分析

The dynamic input-output model is well known in economic theory and practice. In this paper, the asymptotic stability and balanced growth solutions of the dynamic input-output system are considered. Under some natural assumptions which do not require the technical coefficient matrix to be indecomposable,it has been proved that the dynamic input-output system is not asymptotically stable and the closed dynamic input-output model has a balanced growth solution.     

Generating pareto-optimal alternatives by a nonfeasible hierarchical method

A hierarchical algorithm for generating Pareto-optimal alternatives for convex multicriteria problems is derived. At the upper level, values for Lagrange multipliers of the coupling constraints are first given. Then at the subsystems, Pareto-optimal values are determined for the subsystem objectives, whereby an additional term or an additional objective is included due to the Lagrange multipliers. In the subsystem optimizations, the coupling equations between the subsystems are not satisfied; therefore, the method is called nonfeasible. Finally, the upper level checks which of the subsystem solutions satisfy the coupling constraints; these solutions are Pareto-optimal solutions for the overall system.     

An integrated approach for supplier selection in multi-item/multi-supplier environment

One of the important stages in supply chain management which regards all the activities from the purchasing of raw material to final delivery of the product is the supplier selection process. Since it is the first stage of the supply chain management, it is a critical process affecting the consecutive stages. It is simply desired to select the best supplier for a specific product. But since there are a lot of criteria and alternatives to be considered, numerous decision making models have been proposed to provide a solution to this problem. Within this study, an integrated approach including fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and a mixed integer linear programming model is developed to select the best supplier in a multi-item/multi-supplier environment. The importance value of each supplier with respect to each product is obtained via fuzzy TOPSIS in the first stage. Then in the second stage, these values are used as an input in the mathematical model which determines the suppliers and the quantities of products to be provided from the related suppliers. So as to validate the proposed methodology, an application is performed in air filter sector.     

Optimizing a linear function over an integer efficient set

In this paper, a method for optimizing a linear function over the integer Pareto-optimal set without having to determine all integer efficient solutions is presented. The proposed algorithm is based on a simple selection technique that improves the linear objective value at each iteration. Two types of cuts are performed successively until the optimal value is obtained and the current truncated region contains no integer feasible solution.     

Seeking global edges for traveling salesman problem in multi-start search

This study investigates the properties of the edges in a set of locally optimal tours found by multi-start search algorithm for the traveling salesman problem (TSP). A matrix data structure is used to collect global information about edges from the set of locally optimal tours and to identify globally superior edges for the problem. The properties of these edges are analyzed. Based on these globally superior edges, a solution attractor is formed in the data matrix. The solution attractor is a small region of the solution space, which contains the most promising solutions. Then an exhausted enumeration process searches the solution attractor and outputs all solutions in the attractor, including the globally optimal solution. Using this strategy, this study develops a procedure to tackler a multi-objective TSP. This procedure not only generates a set of Pareto-optimal solutions, but also be able to provide the structural information about each of the solutions that will allow a decision-maker to choose the best compromise solution.     

A provably convergent heuristic for stochastic bicriteria integer programming

We propose a general-purpose algorithm APS (Adaptive Pareto-Sampling) for determining the set of Pareto-optimal solutions of bicriteria combinatorial optimization (CO) problems under uncertainty, where the objective functions are expectations of random variables depending on a decision from a finite feasible set. APS is iterative and population-based and combines random sampling with the solution of corresponding deterministic bicriteria CO problem instances. Special attention is given to the case where the corresponding deterministic bicriteria CO problem can be formulated as a bicriteria integer linear program (ILP). In this case, well-known solution techniques such as the algorithm by Chalmet et al. can be applied for solving the deterministic subproblem. If the execution of APS is terminated after a given number of iterations, only an approximate solution is obtained in general, such that APS must be considered a metaheuristic. Nevertheless, a strict mathematical result is shown that ensures, under rather mild conditions, convergence of the current solution set to the set of Pareto-optimal solutions. A modification replacing or supporting the bicriteria ILP solver by some metaheuristic for multicriteria CO problems is discussed. As an illustration, we outline the application of the method to stochastic bicriteria knapsack problems by specializing the general framework to this particular case and by providing computational examples.