基于粗糙集-层次分析法的制造业供应商评价选择

制造企业在确定供应商过程中,必须对供应商进行评价和选择,由于制造业的加工设备和加工工艺的复杂性,同时要确保供应商供货稳定和质量保证等多方面的因素,其评价指标体系必然有别于其它行业的评价指标.采用粗糙集属性约简的方法,对供应商评价选择指标进行约简分析,运用层次分析法对供应商进行评价选择.     

基于组合赋权的村镇商铺供应商综合评价

首次研究了村镇商铺的供应商问题,构建了包含产品成本、供应商诚信度、供应商实力、供应商服务水平、产品质量和供应商发展潜力6个指标的供应商评价指标体系,借助熵权法和方差修正G1法对评价指标进行组合赋权,建立村镇商铺供应商的综合评价模型,以期能够为村镇商铺供应商综合评价问题提供一些借鉴和参考.     

基于神经网络融合技术的供应商评价研究

供应商是整个供应链的源头,他是企业制造资源的输入端,因而对供应商选择是一个企业经营运作的基础.对供应商的评价选择将在很大程度上决定供应链能否平稳运行和运行的效能.BP神经网络方法可避免传统方法的局限性与专家评价的主观随意性,实现了定性分析与定量分析的有效结合,保证供应商评价结果的客观性,但同时也存在训练时间长、易陷入极小值的缺点.因此,建立了一种神经网络融合技术算法.方法是由主成分分析法、粒子群算法与BP算法相融合而成的一种评价方法.通过收敛性分析和数值模拟,验证该算法具有良好的泛化能力,并且在训练误差、训练时间上也要优于BP神经网络算法.因此,对于解决供应商评价问题是有效的.     

基于支持向量机的制造企业创新能力评价模型

在分析了制造企业创新能力评价指标体系的基础上进行企业调查,对收集到的不同类型制造企业的完整数据进行整理,因子分析整理后得到9个综合因子表述原数据,以减少数据处理及问题分析的复杂性.利用支持向量机作为分类器,并使用已有的企业数据作为训练样本,创建了基于支持向量机的制造企业创新能力评价模型.实验结果表明采用径向基函数和多项式函数作为核函数,此模型具有很好的分类性能,可作为制造企业创新能力的评价工具.     

设备可制造性综合评价研究

研究了设备可制造性评价过程，建立了设备可制造性综合评价体系，给出了设备可制造性综合评价的方法。     

基于前景理论和双边匹配决策的合作伙伴初选决策

针对决策信息获取困难的情况采用不同粒度的模糊语言表达属性值,并提出了一种不同粒度模糊语言一致化方法.针对线性加权存在的决策补偿效应,采用基于级别优先序理论的ELECTRE集结基于前景理论的价值函数得到满意度值.传统合作伙伴决策方式只是制造企业对供应商评价并从中选择出评价结果最好的供应商,全然不考虑供应商对制造企业的接受程度,在集中式决策方法中采用双边匹配决策方法构造多目标优化模型,并采用模糊规划中的两阶段法求解多目标优化得到最终匹配结果.实例分析表明该方法的有效性.     

DEA评价与排序的新方法

文章简要回顾了 DEA(Data Envelopment Analysis)方法在先进制造技术 AMT(AdvancedManufacturing Technology)评价以及在其它领域中的研究、应用现状 ;分析了 DEA方法及其改进形式在决策单元有效性评价与排序中存在的问题 ,提出了解决这些问题的新方法     

外源投入型两阶段制造系统网络DEA效率分析研究

制造过程评价是改善制造系统效率的重要一环,传统的评价方法将每个制造系统决策单元视为黑箱来研究整体效率,忽略了中间产品转化信息及投入要素在各子过程中的配置信息。针对两阶段(第二阶段有外源性新投入)制造系统的效率评估问题,分别在固定规模报酬和可变规模报酬假设下,充分利用制造系统中间产品的转化及外源投入要素的配置信息,建立了制造系统网络DEA效率测度及分解模型,建模方法遵循客观评价原则,无需事先主观确定子效率和系统效率之间的组合关系。并将其应用于钢铁制造系统效率测度与分解,研究结果表明该方法能够挖掘决策单元内部子单元的效率情况,帮助决策者发现复杂制造过程非有效的根源,为复杂制造过程的整体效率测度及分解提供了有效的分析方法。     

基于改进NSGA-II算法的云制造服务组合优化研究

云制造任务日趋复杂,与基于云制造的云服务组合优化问题相关的指标日益增多,需要综合考虑各个评价指标,从海量备选云服务中筛选出最优服务组合。本文针对云制造的特点,从线上、线下两方面构建了云制造服务评价指标体系;为了更好地处理高维多目标优化问题并消除实际问题中的量纲影响,本文利用改进的α支配策略代替帕累托支配改进NSGA-II算法,提出了基于nα支配的NSGA-II算法。最后,本文通过一个电机制造案例验证了提出算法的可行性,并通过与标准NSGA-II算法、r-NSGA-II算法和基于模糊支配的NSGA-II算法对比,证明了提出算法得到的解集更优、更小,能够大大减小后续组合优选的计算量。     

指派问题在供应商选优决策中的应用

通常供应链中供应商选优问题为多指标决策问题,本将此问题视为指派问题。指派问题中的关键是确定“效率”矩阵,本充分利用供应商单排序结果,评价指标权重以及供应商指标评价值构造了“效率”矩阵,建立了供应商综合选优指派问题模型,案例试算表明该方法合理、有效,为多指标方案决策提供了又一种可行的决策方法。     

Using SSM to structure the identification of inputs and outputs in DEA

This paper reports on an innovative combination of hard and soft methods—soft systems methodology (SSM) with data envelopment analysis (DEA). Problems in defining and agreeing appropriate inputs and outputs for DEA led to the use of SSM as a way or producing a comprehensive and systemic database of performance indicators. The contributions of the paper are: the use of SSM to improve DEA specifications; conceptual clarifications within both SSM and DEA; and an innovative example of multimethodology. These developments are illustrated through a study evaluating the performance of the basic research institutes of the Chinese Academy of Sciences.     

Characteristics and structures of weak efficient surfaces of production possibility sets

This paper studies the characteristics and structure of the weak surface of the production possibility set. We apply techniques and methods of transferring a polyhedral cone from its intersection form to its sum form, identify an intersection representation of the production possibility set. We give the structure theorem of weak surface of the production possibility set, which includes three complementary slackness conditions. We define the input weak efficiency and output weak efficiency for different DEA models according to the representation of the intersection form. It investigates the characteristics of the weak surfaces, and proves the structure theorems of input weak DEA efficiency and output weak DEA efficiency. The structure theorems establish weighted combination of inputs and outputs that are weak DEA efficient. Numerical examples are provided for illustration.     

Stochastic multicriteria acceptability analysis using the data envelopment model

Data envelopment analysis (DEA) and stochastic multicriteria acceptability analysis (SMAA-2) are methods for evaluating alternatives based on multiple criteria. While DEA is mainly an ex-post tool used for classifying alternatives into efficient and inefficient ones, SMAA-2 is an ex-ante tool for supporting multiple criteria decision-making. Both methods use a kind of value function where the importance of criteria is modeled using weights. Unlike many other methods, neither DEA nor SMAA-2 requires decision-makers’ weights as input. Instead, these so-called non-parametric methods explore the weight space in order to identify weights favorable for each alternative. This paper introduces the SMAA-D method, which is a combination of DEA and SMAA-2. SMAA-D can be characterized as an extension of DEA to handle uncertain or imprecise data to provide stochastic efficiency measures. Alternatively, the combined method can be seen as a variant of SMAA-2 with a DEA-type value function.     

Data envelopment analysis of mutual funds based on second-order stochastic dominance

Although data envelopment analysis (DEA) has been extensively used to assess the performance of mutual funds (MF), most of the approaches overestimate the risk associated to the endogenous benchmark portfolio. This is because in the conventional DEA technology the risk of the target portfolio is computed as a linear combination of the risk of the assessed MF. This neglects the important effects of portfolio diversification. Other approaches based on mean–variance or mean–variance–skewness are non-linear. We propose to combine DEA with stochastic dominance criteria. Thus, in this paper, six distinct DEA-like linear programming (LP) models are proposed for computing relative efficiency scores consistent (in the sense of necessity) with second-order stochastic dominance (SSD). The aim is that, being SSD efficient, the obtained target portfolio should be an optimal benchmark for any rational risk-averse investor. The proposed models are compared with several related approaches from the literature.     

Integrated data envelopment analysis: Global vs. local optimum

Chiou et al. (2010) (A joint measurement of efficiency and effectiveness for non-storable commodities: integrated data envelopment analysis approaches. European Journal of Operational Research 201, 477–489) propose an integrated data envelopment analysis model in measuring decision making units (DMUs) that have a two-stage internal network structure with multiple inputs, outputs, and consumptions. They claim that any optimal solutions determined by their DEA model are a global optimum, not a local optimum. We show that such a conclusion is a false statement due to their misuse of Hessian matrix in examining the concavity of the objective function, and their DEA model is actually a non-convex optimization problem. As a result, their DEA model is unusable in practice due to a lack of efficient algorithm for this particular non-convex DEA model. We further show that Chiou et al.’s (2010) model is a special case of a well-known two-stage network DEA model, and it can be transformed into a parametric linear program for which an approximate global optimal solution can be obtained by solving a sequence of linear programs in combination with a simple search algorithm.     

A new method for ranking non-extreme efficient units in data envelopment analysis

Data envelopment analysis (DEA) evaluates the performance of decision making units (DMUs). When DEA models are used to calculate efficiency of DMUs, a number of them may have the equal efficiency 1. In order to choose a winner among DEA efficient candidates, some methods have been proposed. But most of these methods are not able to rank non-extreme efficient DMUs. Since, the researches performed about ranking of non-extreme efficient units are very limited, incomplete and with some difficulties, we are going to develop a new method to rank these DMUs in this paper. Therefore, we suppose that DMU _o is a non-extreme efficient under evaluating DMU. In continue, by using “Representation Theorem”, DMU _o can be represented as a convex combination of extreme efficient DMUs. So, we expect the performance of DMU _o be similar to the performance of convex combination of these extreme efficient DMUs. Consequently, the ranking score of DMU _o is calculated as a convex combination of ranking scores of these extreme efficient DMUs. So, the rank of this unit will be determined.     

An input relaxation measure of efficiency in stochastic data envelopment analysis

We introduce stochastic version of an input relaxation model in data envelopment analysis (DEA). The input relaxation model, recently developed in DEA, is useful to resource management [e.g. G.R. Jahanshahloo, M. Khodabakhshi, Suitable combination of inputs for improving outputs in DEA with determining input congestion, Appl. Math. Comput. 151(1) (2004) 263–273]. This model allows more changes in the input combinations of decision making units than those in the observed inputs of evaluating decision making units. Using this extra flexibility in input combinations we can find better outputs. We obtain a non-linear deterministic equivalent to this stochastic model. It is shown that under fairly general conditions this non-linear model can be replaced by an ordinary deterministic DEA model. The model is illustrated using a real data set.     

Generalizing cross redundancy in data envelopment analysis

Lee and Choi (2010) proved that a cross redundant output in a CCR or BCC DEA study is unnecessary and can be eliminated from the model without affecting the results of the study. A cross redundant output, as characterized by Lee and Choi, can be expressed as a specially constrained linear combination of both some outputs and some inputs. This article extends the contributions of Lee and Choi (2010) in at least three ways: (i) by adding precision and clarity to some of their definitions; (ii) by introducing specific definitions that complement the ones in their paper; and (iii) by conducting some additional analysis on the impact of the presence of other types of linear dependencies among the inputs and outputs of a DEA model. One reason that it is important to identify and remove cross redundant inputs or outputs from DEA models is that the computational burden of the DEA study is decreased, especially in large applications.     

The LeChatelier principle in a DEA model

This paper aims to relate the LeChatelier principle, first introduced into economics by Samuelson (1947), with the DEA approach through two propositions. These propositions allow for bridging the principle over a DEA model with and without the presence of non-discretionary inputs and enable one to make comparisons for the various efficiency measures under different conditions. The quasi-fixity of some inputs hinders a firm’s capacity from instantly and freely adjusting its input combination in order to minimize its production costs. The assumption that all inputs are discretionary tends to exaggerate managers’ ability to dispense resources and renders invalid information on the adjustment of the current input mix.     

Network DEA pitfalls: Divisional efficiency and frontier projection under general network structures

Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs). Recently network DEA models been developed to examine the efficiency of DMUs with internal structures. The internal network structures range from a simple two-stage process to a complex system where multiple divisions are linked together with intermediate measures. In general, there are two types of network DEA models. One is developed under the standard multiplier DEA models based upon the DEA ratio efficiency, and the other under the envelopment DEA models based upon production possibility sets. While the multiplier and envelopment DEA models are dual models and equivalent under the standard DEA, such is not necessarily true for the two types of network DEA models. Pitfalls in network DEA are discussed with respect to the determination of divisional efficiency, frontier type, and projections. We point out that the envelopment-based network DEA model should be used for determining the frontier projection for inefficient DMUs while the multiplier-based network DEA model should be used for determining the divisional efficiency. Finally, we demonstrate that under general network structures, the multiplier and envelopment network DEA models are two different approaches. The divisional efficiency obtained from the multiplier network DEA model can be infeasible in the envelopment network DEA model. This indicates that these two types of network DEA models use different concepts of efficiency. We further demonstrate that the envelopment model’s divisional efficiency may actually be the overall efficiency.