考虑竞争视野与利益相关的DEA交叉效率分析

doi:10.3969/j.issn.1001-506X.2020.04.20

摘要/Abstract

摘要：

在实际竞争与合作问题中,由于合作者或竞争者的规模等条件不同,决策单元(decision-making unit, DMU)之间的利益和竞争强度存在差异性,但传统的数据包络分析(data envelopment analysis, DEA)交叉效率模型并未考虑这一因素。因此,提出考虑DMU之间竞争关注度与净利益系数的DEA交叉效率评价方法,并且为了考虑综合信息,采用信息熵的方法对不同交叉效率模型的效率值进行综合得出DMU的加权效率。与传统的二次目标模式相比,该方法较好地体现了DMU之间利益和竞争强度存在的差异性,同时可以使被评价单元更有针对性地实施对合作者的支持手段或对竞争者的打压措施,从而更好地增强DMU的群体优势。

关键词: 综合评价, 数据包络分析, 交叉效率

Abstract:

In the actual competition and cooperation problems, due to the scale difference of the partners or competitors, there exist differential in the interests and competition strength between the decision-making units (DMUs), but the traditional data envelopment analysis (DEA) cross-efficiency model does not consider this factor. Therefore, this paper proposes a DEA cross-efficiency analysis method that considers the competition degree and the stakeholder coefficient between the DMUs. In order to consider the comprehensive information, the information entropy method is used to synthesize the efficiency values of different cross-efficiency models. Compared with the traditional quadratic target model, this method reflects the differences in the degree of interests and competition between the DMUs, and enables the evaluated unit to implement the support means for the partners or the suppression measures for the competitors to different degrees, which can enhance the group advantage of DMU.

Key words: comprehensive evaluation, data envelopment analysis (DEA), cross-efficiency

中图分类号:

斯琴, 马占新, 刘智慧. 考虑竞争视野与利益相关的DEA交叉效率分析[J]. 系统工程与电子技术, 2020, 42(4): 887-895.

Qin SI, Zhanxin MA, Zhihui LIU. DEA cross-efficiency analysis considering competitive perspective and stakeholder[J]. Systems Engineering and Electronics, 2020, 42(4): 887-895.

图/表 5

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DMU	x₁	x₂	y₁	CCR效率值	模型P₁
DMU₁	2	12	1	1.000	0.660 2
DMU₂	2	8	1	1.000	0.785 7
DMU₃	5	5	1	1.000	0.720 0
DMU₄	10	4	1	1.000	0.680 0
DMU₅	10	6	1	0.750	0.513 3

部门	投入指标			产出指标			CCR效率
部门	x₁	x₂	x₃	y₁	y₂	y₃	CCR效率
1	12	400	20	60	35	17	1.000 0
2	19	750	70	139	41	40	1.000 0
3	42	1 500	70	225	68	75	1.000 0
4	15	600	100	90	12	17	0.819 7
5	45	2 000	250	253	145	130	1.000 0
6	19	730	50	132	45	45	1.000 0
7	41	2 350	600	305	159	97	1.000 0

DMU	模型C₁	模型C₂	模型S₁	模型S₂
1	0.837 6	0.739 4	0.939 8	0.841 6
2	0.718 3	0.736 7	0.953 9	0.957 9
3	0.767 0	0.745 3	0.792 9	0.794 2
4	0.387 2	0.426 6	0.608 3	0.655 3
5	0.657 4	0.728 9	0.878 5	0.842 8
6	0.842 4	0.850 6	0.992 9	0.988 1
7	0.529 4	0.636 2	0.898 8	0.879 7

DMU	模型P₁	模型P₂	模型C₁	模型C₂	模型S₁	模型S₂	综合效率
1	1	3	2	3	3	5	2
2	4	2	4	4	2	2	3
3	3	6	3	2	6	6	4
4	7	7	7	7	7	7	7
5	5	5	5	5	5	4	5
6	2	1	1	1	1	1	1
7	6	4	6	6	4	3	6

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