基于公共权重的区间DEA效率评价及其排序方法研究

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  • 福州大学经济与管理学院决策科学研究所, 福建福州 350116
蓝以信  E-mail: lyx0302205@163.com

收稿日期: 2019-09-18

  网络出版日期: 2021-12-11

基金资助

国家自然科学基金(71701050);国家自然科学基金(71804024);国家自然科学基金(71801050);福建省社科研究基地重大研究项目(FJ2020MJDZ016);福建省自然科学基金(2016J05171);福建省自然科学基金(2021J01568);福州大学“旗山学者”计划项目(GXRC201807)

A common-weights interval DEA approach for efficiency evaluation and its ranking method

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  • Institute of Decision Science, School of Economics and Management, Fuzhou University, Fuzhou 350116, Fujian, China

Received date: 2019-09-18

  Online published: 2021-12-11

摘要

针对传统区间数据包络分析方法,在确定每一个决策单元区间效率的上界和下界时,存在的评价尺度不一致且计算复杂等问题,本文提出了一种同时最大化所有决策单元的效率上界和下界的公共权重区间DEA模型,并给出了一种考虑决策者偏好信息的可能度排序方法,用以解决区间效率的全排序问题。最后,以中国大陆11个沿海省份工业生产效率测算为例说明了所提方法的有效性和实用性。

本文引用格式

蓝以信, 温槟檐, 王应明 . 基于公共权重的区间DEA效率评价及其排序方法研究[J]. 运筹学学报, 2021 , 25(4) : 58 -68 . DOI: 10.15960/j.cnki.issn.1007-6093.2021.04.005

Abstract

In order to solve the inconsistency of the evaluation scale in efficiency evaluation in interval data envelopment analysis, this paper proposes a common weight interval DEA model based on the target by maximizing the sum of the upper bound efficiency and the lower bound efficiency of all decision-making units (DMUs). Thus, our approach not only makes the interval efficiency of all DMUs being comparable, but also ensures that the upper bound efficiency and the lower bound efficiency are evaluated in the same scale. To rank the interval efficiencies completely a new approach which is based on the defined possibility degree formula in considering of the decision makers' interval preference is proposed. Finally, an example of measuring the industrial efficiencies in 11 coastal provinces of China is investigated to illustrate the effectiveness and the usefulness of our approaches.

参考文献

1 Charnes A , Cooper W W , Rhodes E . Measuring the efficiency of decision making units[J]. European Journal of Operational Research, 1978, 2 (6): 429- 444.
2 Chen L , Huang Y , Li M J , et al. Meta-frontier analysis using cross-efficiency method for performance evaluation[J]. European Journal of Operational Research, 2020, 280 (1): 219- 229.
3 Wu J , Chu J , Sun J , et al. DEA cross-efficiency evaluation based on Pareto improvement[J]. European Journal of Operational Research, 2016, 248 (2): 571- 579.
4 An Q , Wen Y , Ding T , et al. Resource sharing and payoff allocation in a three-stage system: Integrating network DEA with the Shapley value method[J]. Omega, 2019, 85, 16- 25.
5 Chen K , Cook W D , Zhu J . A conic relaxation model for searching for the global optimum of network data envelopment analysis[J]. European Journal of Operational Research, 2020, 280 (1): 242- 253.
6 Ruiz J L , Sirvent I . Common benchmarking and ranking of units with DEA[J]. Omega, 2016, 65, 1- 9.
7 Banker R D , Chang H , Natarajan R . Productivity change, technical progress, and relative efficiency change in the public accounting industry[J]. Management Science, 2005, 51 (2): 291- 304.
8 Pastor J T , Lovell C A K , Aparicio J . Defining a new graph inefficiency measure for the proportional directional distance function and introducing a new Malmquist productivity index[J]. European Journal of Operational Research, 2020, 281 (1): 222- 230.
9 Wu J , Chu J , An Q , et al. Resource reallocation and target setting for improving environmental performance of DMUs: An application to regional highway transportation systems in China[J]. Transportation Research Part D: Transport and Environment, 2018, 61, 204- 216.
10 Chen C M , Zhu J . Efficient resource allocation via efficiency bootstraps: an application to R & D project budgeting[J]. Operations Research, 2011, 59 (3): 729- 741.
11 Podinovski V V . Returns to scale in convex production technologies[J]. European Journal of Operational Research, 2017, 258 (3): 970- 982.
12 张晓明, 王应明, 施海柳. 考虑非期望规模收益的创新型企业并购决策[J]. 运筹学学报, 2018, 22 (1): 42- 54.
13 Cooper W W , Park K S , Yu G . IDEA and AR-IDEA: Models for dealing with imprecise data in DEA[J]. Management Science, 1999, 45 (4): 597- 607.
14 Wang Y M , Greatbanks R , Yang J B . Interval efficiency assessment using data envelopment analysis[J]. Fuzzy Sets and Systems, 2005, 153 (3): 347- 370.
15 Kao C . Interval efficiency measures in data envelopment analysis with imprecise data[J]. European Journal of Operational Research, 2006, 174 (2): 1087- 1099.
16 Yang F , Ang S , Xia Q , et al. Ranking DMUs by using interval DEA cross efficiency matrix with acceptability analysis[J]. European Journal of Operational Research, 2012, 223 (2): 483- 488.
17 范建平, 岳未祯, 吴美琴. 基于误差传递和熵的区间DEA方法[J]. 系统工程理论与实践, 2015, 35 (5): 1293- 1303.
18 Azizi H , Kordrostami S , Amirteimoori A . Slacks-based measures of efficiency in imprecise data envelopment analysis: An approach based on data envelopment analysis with double frontiers[J]. Computers & Industrial Engineering, 2015, 79, 42- 51.
19 蓝以信, 王旭, 王应明. 区间型产出下的DEA-Malmquist生产率指数及其应用研究[J]. 系统科学与数学, 2017, 37 (6): 1494- 1508.
20 Sinuany-Stern Z , Friedman L . DEA and the discriminant analysis of ratios for ranking units[J]. European Journal of Operational Research, 1998, 111 (3): 470- 478.
21 许皓, 孙燕红, 华中生. 基于整体效率的区间DEA方法研究[J]. 中国管理科学, 2010, 18 (2): 102- 107.
22 Hatami M A , Tavana M , Agrell P J , et al. A common-weights DEA model for centralized resource reduction and target setting[J]. Computers & Industrial Engineering, 2015, 79, 195- 203.
23 Lotfi F H , Hatami-Marbini A , Agrell P J , et al. Allocating fixed resources and setting targets using a common-weights DEA approach[J]. Computers & Industrial Engineering, 2013, 64 (2): 631- 640.
24 Mehrabian S , Jahanshahloo G R , Alirezaee M R , et al. An assurance interval for the non-Archimedean epsilon in DEA models[J]. Operations Research, 2000, 48 (2): 344- 347.
25 Amin G R , Toloo M . A polynomial-time algorithm for finding $ \varepsilon $ in DEA models[J]. Computers & Operations Research, 2004, 31 (5): 803- 805.
26 成达建, 薛声家. 基于交叉效率新计算方法的区间效率值排序[J]. 中国管理科学, 2017, 25 (7): 191- 196.
27 李德清, 谷云东. 一种基于可能度的区间数排序方法[J]. 系统工程学报, 2008, 23 (2): 243- 246.
28 李德清, 韩国柱, 曾文艺, 等. 基于布尔矩阵的区间数排序方法[J]. 控制与决策, 2016, 31 (4): 629- 634.
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