基于主从博弈的两部门并行系统的效率评价

本文研究了一类典型并行系统的效率评价问题：（1）决策单元由两个并行的子单元组成;（2）在整个系统中,某一子单元居于主导地位,另一子单元居于从属地位;（3）两个子单元之间存在部分共享的投入资源,且无法明显区别该资源在不同子单元之间的分配比例。在分析决策单元整体效率及内部子单元效率的基础上,基于主从博弈思想,提出一种能同时确定系统整体效率及内部子单元效率的评价方法,该方法能够在评价系统效率的同时,实现共享资源的有效分配。最后,采用一个实例分析说明了所提方法的合理性和有效性。     

非合作博弈两阶段生产系统DEA并购效率评价

如何选择并购对象及预测并购可行性是企业管理者在并购决策过程中常面临的问题。针对企业并购预测问题,本文以两阶段生产系统的决策单元为研究对象,研究数据包络分析方法在预测并购可行性方面的应用。该生产系统有两个明显的特点:(1)决策单元由两个阶段串行子系统构成,(2)两个子系统一个处于主导地位,而另一个处于从属地位。两个或者两个以上决策单元并购为一个虚拟决策单元。本文在分析由决策单元整体效率与其子系统效率之间关系的基础上,引入非合作博弈的思想,提出了基于非合作博弈的DEA模型评估虚拟决策单元两阶段生产系统的并购效率:即在保持现有的产出水平和生产效率的前提下,分别求解虚拟决策单元及其子系统的并购效率,分析虚拟决策单元如何通过两个子系统实现整个系统的成本节约。最后,将模型应用于台湾24家非寿险保险公司的仿真分析。由于文章提出的方法能够有效地分析虚拟决策单元内部子系统的并购有效性水平,同时考虑子系统之间的主从关系,因此,能够发掘影响虚拟决策单元并购效率的内部因素,因而能够为管理者并购决策提供更精确有效的管理信息,提高并购决策的科学性。     

基于整体效率的区间DEA方法研究

针对现有的区间DEA方法采用非统一指标数据进行效率评价的不足,本文在分析现有区间DEA方法的基础上,提出一种基于整体效率的区间DEA方法。该方法能够在多决策单元系统整体效率最大化的同时,得到统一的各决策单元投入/产出的精确数据及各指标权重,一次性求解出所有决策单元的效率。最后,采用一个算例分析说明文章所提方法的合理性和优越性。     

评价相对效率的投入—产出型DEA

传统 DEA只能在固定投入或产出的条件下 ,从产出或投入角度测算决策单元相对效率 ,因而不能综合地反映决策单元的投入产出效果 .基于双目标规划 ,本文将提出从投入及产出角度评价决策单元相对效率的投入—产出型 DEA,并研究其相对有效性 .最后以沪市 1 6家高科技上市公司为应用实例 ,研究其相对经营效率     

评价相对效率的投入-产出型DEA

传统DEA只能在固定投入或产出的条件下,从产出或投入角度测算决策单元相对效率,因而不能综合地反映决策单元的投入产出效果.基于双目标规划,本文将提出从投入及产出角度评价决策单元相对效率的投入-产出型DEA,并研究其相对有效性.最后以沪市16家高科技上市公司为应用实例,研究其相对经营效率.     

并联系统资源和目标配置双准则DEA模型

针对决策单元内部资源和目标配置问题,以并联生产系统为研究对象,研究数据包络分析方法在组织的资源分配与目标设定方面的应用.首先考虑决策单元内部评价的特点:(1)固定投入和固定产出;(2)子单元评价的权重选择之间的关系;(3)内部评价权重选择与决策单元整体评价的关系.在此基础上,提出基于效率与公平的决策单元内部资源和目标配置双准则DEA模型,最后用算例演示其使用,以说明模型的合理性.     

基于未来效率的兼顾公平与效率的资源分配DEA模型研究——以各省碳排放额分配为例

根据需要实现的目标，评估各生产决策单元的投入产出效率及改进潜力，可以为资源分配提供重要参考。构建一个基于未来效率的兼顾公平与效率的资源分配DEA模型可以有效、灵活地解决该问题。该DEA模型首先根据历史数据计算每个生产决策单元过去各期的技术增长率，并预测各DMU未来的技术增长率，从而获得未来的生产前沿面。以此为基础，在九个硬性目标约束下，分三个步骤分别解决三个追求的软性目标：最大化期望总产出、最小化非期望总产出、最小化可变要素总投入。管理者不仅可以改变九个硬性目标的参数值，以及调整三个软性目标的优先顺序和软性目标参数值，进而形成各种兼顾效率与公平的资源分配方案。最后，运用该DEA模型评估了我国各省碳排放削减潜力，并形成了能够实现管理者期望目标的各省碳排放削减责任分配方案。     

存在非期望产出的决策单元阻塞问题研究

阻塞是指企业的过量投入引起生产效益下降的经济现象,近年来已经受到实业界与学术界的广泛关注。现有的决策单元阻塞问题研究普遍假设产出均是期望指标,然而,在实际生产中很多产出属于非期望指标。本文构建了同时存在期望产出和非期望产出决策单元的阻塞度量模型,该模型不仅可以判别决策单元是否存在阻塞,而且可以测算各项投入的阻塞程度,并给出了决策单元阻塞和技术无效之间的关系。最后,运用所提出的模型对我国上市商业银行的效率和阻塞情况进行了测算与分析,验证了本文模型的实用性与可行性。     

效用型Malmquist指数方法及火电类上市公司实证分析

文章定义了一种基于距离函数的效用型Malmquist指数,并将其分解为技术效率、技术进步、配置效率和系统外生效应等多个指标,该指数采用"期望效用最大"而非"期望产出最大"的优化原则寻找最优决策单元。该指数适用于寡头垄断行业或需要综合考虑经济效益、社会影响的社会公共品行业。最后以2006到2007年中国火电类上市公司进行实证分析,证明与投入型Malmquist指数相比,效用型Malmquist指数评价结果更为客观,经济解释更加明确。     

基于逐次DEA的矿产资源开发效率评价

从劳动力投入、资本投入、土地等自然资源投入,经济效益产出、环境效益产出等方面来构建矿产资源开发评价指标体系。采用逐次数据包络分析(S-DEA)方法来确定各级效率前沿面,实现对各决策单元的矿产资源开发效率的分级划分。以河北省2011年各县区铁矿资源开发效率评价,实例验证了指标体系的可用性与S-DEA方法的有效性。     

考虑满意度的两阶段DEA固定成本分摊方法

考虑具有相同的投入和产出的两阶段决策单元的固定成本分摊问题。将待分摊的固定成本作为一种新的投入,建立两阶段加性DEA模型。首先证明了各决策单元从自身角度出发可以找到至少一种分摊方案,使其自身整体和阶段效率都达到Pareto有效。然后证明了在公共的权重下,至少存在一种分摊方案可以使得各决策单元整体和阶段效率都达到Pareto有效。并给出了这样的有效成本分摊方案集。为了得到公平的成本分摊方案,定义了各DMU各阶段对分摊方案的满意度,最大化最小的满意度,可以得到最终的成本分摊方案。将问题拓展到两阶段存在共享投入的生产系统中,这些性质定理同样成立。数值算例部分将本文与Yu等（2016）的研究结果进行了对比,验证了本文方法的有效性,指出了本文方法的优越性。本文的方法综合考虑了效率和公平,得到的固定成本分摊方案公平合理。     

Centralized resource allocation based on efficiency analysis for step-by-step improvement paths

The existing centralized resource allocation models often assume that all of the DMUs are efficient after resource allocation. For the DMU with a very low efficiency score, it means the dramatic reduction of the resources, which can cause the organizational resistance. In addition, in reality, it is particularly difficult for the DMUs to achieve their target efficiencies in a single step, especially when they are far from the efficient frontier. Thus, gradual progress towards benchmarking targets is gaining importance. In this paper, we present a new approach for resource allocation based on efficiency analysis under a centralized decision-making environment. Through our approach, the central decision-maker can obtain a sequence of intermediate benchmark targets based on efficiency analysis, which provide a level-wise improvement path to direct the DMUs to reach their ultimate targets on the efficient frontier in an implementable and realistic manner. Numerical examples are presented to illustrate the application procedure of the proposed approach.     

Evaluating the administrative efficiency of courts

In addition to being held accountable for judicial decision, courts, like other public agencies, can and should be evaluated in terms of their administrative efficiency. This paper illustrates how courts can be evaluated in terms of their relative administrative efficiency, using a new approach—data envelopment analysis (DEA)—first proposed by Charnes et al. [1]. The DEA is based upon the economic notion of Pareto optimality which states that a given decision making unit (DMU) is inefficient if some other DMU, or some combination of other DMUs, can produce at least the same amounts of all outputs with less of some resource input and not more of any other resource. Conversely a DMU is said to be efficient if the above is not possible. Charnes et al. [1] generalized the usual input/output ratio measure of efficiency for a given unit in terms of a fractional linear program with fractional constraints. In the case of courts, the efficiency of any particular court is calculated by forming the ratio of a weighted sum of outputs to a weighted sum of inputs, where the weights for both outputs and inputs are to be selected in a manner that calculates the Pareto-Koopmans efficiency of the court. This paper reviews the DEA method and illustrates its application to a data base for 100 criminal superior courts in North Carolina.     

Allocating a fixed cost based on data envelopment analysis and satisfaction degree

This paper uses the Data Envelopment Analysis (DEA) technique to solve the problem of allocating a fixed cost across a set of comparable decision making units (DMUs) in a fair way. It first investigates the effect of the fixed cost on each DMU and on the collection of DMUs. Next we prove that there exist some cost allocations which can make each DMU and the collection of DMUs efficient. We show that such a cost allocation is unique and equivalent to the proportional sharing method if the fixed cost allocation problem is a one-dimensional case. In a multidimensional case, the fixed cost allocations may not be unique. This paper defines the concept of satisfaction degree, and proposes a maxmin model and a corresponding algorithm to generate a unique fixed cost allocation. Finally, the proposed approach has been applied to a data set from prior literature.     

Imposing the Regular Ultra Passum law in DEA models

Recently there has been some discussion in the literature concerning the nature of scale properties in the Data Envelopment Model (DEA). It has been argued that DEA may not be able to provide reliable estimates of the optimal scale size. We argue in this paper that DEA is well suited to estimate optimal scale size, if DEA is augmented with two additional maintained hypotheses which imply that the DEA-frontier is consistent with smooth curves along rays in input and in output space that obey the Regular Ultra Passum (RUP) law, i.e. monotonically decreasing scale elasticities. A necessary condition for a smooth curve passing through all vertices to obey the RUP-law is presented. If this condition is satisfied then upper and lower bounds for the marginal product at each vertex are presented. It is shown that any set of feasible marginal products will correspond to a smooth curve passing through all points with a monotonic decreasing scale elasticity. The proof is constructive in the sense that an estimator of the curve is provided with the desired properties. A typical DEA based return to scale analysis simply reports whether or not a DMU is at the optimal scale based on point estimates of scale efficiency. A contribution of this paper is that we provide a method which allows us to determine in what interval optimal scale is located.     

基于随机理论的多项目资源配置模型及应用研究

为解决多项目管理中共享资源配置问题,在对信息环境下企业资源特点研究的基础上,应用随机理论确定了企业资源多项目并行配置时的资源等效效率和效率转换系数,由此建立了资源配置效率模型,通过对其数学方程的分析给出了相应的算法.运用该模型和算法,通过实例分析实现了资源的合理配置.     

随机加权交叉效率下的资源分配问题研究

资源在经济发展中的地位变得越来越重要,本文针对资源分配中存在的某些影响因素是随机的,提出利用随机DEA方法,从三方面综合分析研究资源分配问题,即从生产效率、投入产出弹性和生产潜能。提出并建立了新的随机加权交叉效率计算模型,建立了潜在生产能力模型,给出了规模回报计算公式。综合三个影响因素,建立了新的资源分配权重计算模型。经算例验证,其计算结果令人满意。     

Intra-Organizational and inter-organizational resource allocation in two-stage network systems

There are organizational systems, such as bank branches and two-stage supply chains, which are composed of multiple parallel two-stage structures. Resource allocation in these systems is to maximize the benefit of the overall organization from a global viewpoint. In this study, we consider two types of systems at an organizational level: a centralized organizational system treating the whole two-stage production process as a basic unit, and a decentralized organizational system including two sub-organizations (groups) treating one of the two-stage production processes as a basic unit. We propose intra-organizational and inter-organizational resource allocation plans for two different organizational systems, respectively. Specially, two modes of free intermediate resource allocation (Free IRA) and fixed intermediate resource allocation (Fixed IRA) are discussed for the decentralized organizational system. The proposed allocation plans are based on two-stage data envelopment analysis models with bi-level formulations, in which the upper-level model is to maximize the entire organizational effectiveness (total outputs minus total inputs) by determining the optimized input resources and output targets while the lower-level model is concerned with efficiency constraints of all decision-making units simultaneously. The developed methods are illustrated by an application to a real-world problem with 17 city bank branches.     

A slacks-based measure model for calculating cross efficiency in data envelopment analysis

Cross efficiency is a concept for solving the problem of incomparability among the efficiencies of a set of decision making units (DMUs) calculated from different weights in data envelopment analysis, and is helpful for ranking. Conventional cross efficiency is a radial measure, and the radial measure has some weaknesses. First, it is not able to provide appropriate efficiency scores for weakly efficient DMUs. Second, the efficiencies measured from the input and output sides under variable returns to scale are different which may cause difficulties in subsequent analyses. Third, negative values may appear when the efficiency is measured from the input side. To solve these problems, this paper proposes a slacks-based measure to calculate efficiency. The basic idea is the same as that of the radial measure using the frontier corresponding to a DMU to measure the efficiency of all DMUs, except that the efficiency measure is slacks-based instead of radial. Due to the nature of the slacks-based efficiency measure, the problems caused by weak efficiency and difference in input and output radial measures do not exist. More importantly, negative efficiencies are not produced. The proposed method is applied to a real case involving the selection of the most efficient robot to use for production. The results help identify the top-ranked robot.