基于Cross-efficiency DEA的中间站运营绩效分析

根据传统DEA存在的局限性,在有关文献研究的基础上,提出了基于Cross-efficiency DEA模型的中间站绩效分析方法,在调查收集影响中间站运营绩效基础数据的基础上,利用灰色关联分析法提取了评价指标体系,采用Cross-efficiency DEA模型对中间站运营绩效进行了分析、评价和测算,并运用MATLAB软件编程进行了求解,获得了各中间站源配置效率的排序,在对非DEA有效的决策单元进行投影分析的基础上,识别出影响决策单元运营绩效的关键因素,为改进非DEA有效决策单元提供了一个可行方案。最后利用实例验证了该模型的有效性、科学性,分析结果表明:Cross-efficiency DEA模型基本能够反映决策单元运行效率的现实状况。

基于Cross-efficiency DEA算法的区域科技资源配置效率测算研究

伴随信息化水平的日益提高,我国科技资源组织结构和运行效率进一步优化。然而由于我国区域经济发展的非均衡性问题,导致了我国科技资源配置的区域差异性。本文引入Cross-efficiency DEA算法,从科技资源投入、产出的视角对我国30个省、市、自治区对应的科技资源配置效率进行了测算。利用Cross-efficiency在群体综合评价中能够完全排序的优点,可以获得区域资源配置效率的排序结果,从而为区域科技管理提供有效的参考信息。

Ranking approach of cross-efficiency based on improved TOPSIS technique

The cross-efficiency evaluation method is reviewed which is developed as a data envelopment analysis （DEA） extensive tool. The cross-efficiency evaluation method is utilized to identify the decision making unit （DMU） with the best practice and to rank the DMUs by their respective cross-efficiency scores. The main drawbacks of the cross-efficiency evaluation method when the ultimate average cross-efficiency scores are used to evalu- ate and rank the DMUs are also pointed out. With the research gap, an improved technique for order preference by similarity to ideal solution （TOPSIS） is introduced to rank the crossfficiency by eliminating the average assumption. Finally, an empirical example is illustrated to examine the validity of the proposed method.

Promoting Balance in Output Efficiencies for Cross-Efficiency Evaluation in Data Envelopment Analysis

Cross-efficiency evaluation is recognized as an effective way of efficiency assessment for a set of decision making units (DMUs) in the framework of data envelopment analysis (DEA). It has been generally suggested that secondary goals be introduced for cross-efficiency evaluation owing to the non-uniqueness of optimal solutions in self-evaluation. This paper develops a variety of secondary goals in the spirit of promoting balance in the output efficiencies of the DMU under evaluation. The proposed models attempt to make each output contribute as equally as possible to the self-evaluated efficiency. In this way, the weight flexibility can for one thing be reduced by the introduced secondary goals with selections from alternate optimal solutions, in addition to counting on the dilution of flexibility in the subsequent peer-evaluation. The proposed approach might be applicable to evaluation problems in which multiple outputs are considered important and balance is encouraged to put all dimensions into sufficient use. The effectiveness of the proposed approach and its comparisons with some relevant secondary goals are illustrated empirically using numerical examples.

Cross-efficiency DEA model-based evaluation of allocative efficiency of rural information resources in Jiangsu Province, China

Purpose: This paper aims to compare and rank the allocative efficiency of information resources in rural areas by taking 13 rural areas in Jiangsu Province, China as the research sample.Design/methodology/approach: We designed input and output indicators for allocation of rural information resources and conducted the quantitative evaluation of allocative efficiency of rural information resources based on cross-efficiency model in combination with the classical CCR model in data envelopment analysis(DEA).Findings: Cross-efficiency DEA model can be used for our research with the objective to evaluate quantitatively and objectively whether the allocation of information resources in various rural areas is reasonable and whether the output is commensurate with the input.Research limitations: We have to give up using some indicators because of limited data availability. There is a need to further improve the cross-efficiency DEA model because it cannot identify the specific factors influencing the efficiency of decision-making units(DMUs).Practical implications: The evaluation results will help us understand the present allocative efficiency levels of information resources in various rural areas so as to provide a decisionmaking basis for formulation of the policies aimed at promoting the circulation of information resources in rural areas.Originality/value: Little or no research has been published about the allocative efficiency of rural information resources. The value of this research lies in its focus on studying rural informatization from the perspective of allocative efficiency of rural information resources and in the application of cross-efficiency DEA model to evaluate allocative efficiency of rural information resources as well.

Measuring the NBA Teams’ Cross-Efficiency by DEA Game

In this paper, we use DEA to measure the NBA basketball teams’ efficiency in seasons 2006-2007, 2007-2008, 2008-2009 and 2009-2010. In this context, each team is a DMU;we select the payroll and the average attendance to be the inputs while the wins and the average points per game to be the outputs. First, in order to obtain benchmarks, we measure the DMUs efficiency through classic DEA BCC model with an assurance region for each one of the four seasons individually and together. When we consider the four seasons together, we may analyse whether the performance of each team increases or decreases over time. Next, we evaluate the teams cross efficiency by DEA game to consider that there is no cooperation among DMUs. This approach also improves the efficiencies discrimination.

A RANKING METHOD FOR DMUS WITH INTERVAL DATA BASED ON DEA CROSS-EFFICIENCY EVALUATION AND TOPSIS

The traditional data envelopment analysis （DEA） model can evaluate the relative efficiencies of a set of decision making units （DMUs） with exact values of inputs and outputs, but it cannot handle imprecise data. Imprecise data, for example, can be expressed in the form of the interval data or mixtures of interval data and ordinal data. In this study, a cross-efficiency method is introduced into the DEA model to calculate the interval of cross-efficiency values, based on which a new TOPSIS method is proposed to rank the DMUs. Two examples are presented to illustrate and validate the proposed method.

Methods and applications of DEA cross-efficiency:Review and future perspectives

The field of engineering management usually involves evaluation issues,such as program selection,team performance evaluation,technology selection,and supplier evaluation.The traditional self-evaluation data envelopment analysis(DEA)method usually exaggerates the effects of several inputs or outputs of the evaluated decision-making unit(DMU),resulting in unrealistic results.To address this problem,scholars have proposed the cross-efficiency evaluation(CREE)method.Compared with the DEA method,CREE can rank DMUs more completely by using reasonable weights.With the extensive application of this technique,several problems,such as non-unique weights and non-Pareto optimal results,have arisen in CREE methods.Therefore,the improvement of CREE has attracted the attention of many scholars.This paper reviews the theory and applications of CREE,including the non-uniqueness problem,the aggregation of cross-efficiency data,and applications in engineering management.It also discusses the directions for future research on CREE.

An Alternative Approach to Rank Efficient DMUs in DEA via Cross-Efficiency Evaluation,Gini Coefficient,and Bonferroni Mean

In this paper,we focus on a critical problem in data envelopment analysis(DEA)and propose a simple resolution for it.The major problem of the DEA is the existence of several efficient decision-making units(DMUs).To deal with this issue,we introduce a method that involves cross-efficiency evaluation,Gini coefficient,and Bonferroni mean.First,a cross-efficiency matrix is developed.Then,mixing the Gini coefficient and Bonferroni mean,a Gini–Bonferroni(GB)index is proposed for ranking efficient DMUs,where the DMUs with bigger GB are ranked higher.The proposed method broke the tie between efficient DMUs.Finally,a numerical example and real application of this method are presented in the ranking of research and development(R&D)investment companies in the pharmaceutical and biotechnology industries.

Discriminating Among Relatively Efficient Units in Data Envelopment Analysis: A Comparison of Alternative Methods and Some Extensions

This paper concentrates on methods for comparing activity units found relatively efficient by data envelopment analysis (DEA). The use of the basic DEA models does not provide direct information regarding the performance of such units. The paper provides a systematic framework of alternative ways for ranking DEA-efficient units. The framework contains criteria derived as by-products of the basic DEA models and also criteria derived from complementary DEA analysis that needs to be carried out. The proposed framework is applied to rank a set of relatively efficient restaurants on the basis of their market efficiency.