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中国管理科学 ›› 2014, Vol. 22 ›› Issue (11): 62-71.

• 论文 • 上一篇    下一篇

决策单元交叉效率的自适应群评价方法

张启平1, 刘业政2,3, 姜元春2,3   

  1. 1. 合肥工业大学计算机与信息学院, 安徽 合肥 230009;
    2. 合肥工业大学管理学院, 安徽 合肥 230009;
    3. 过程优化与智能决策教育部重点实验室, 安徽 合肥 230009
  • 收稿日期:2012-05-03 修回日期:2013-02-21 出版日期:2014-11-20 发布日期:2014-11-21
  • 作者简介:张启平(1981-),男(汉族),安徽马鞍山人,合肥工业大学计算机与信息学院助理研究员,博士,研究方向:决策科学与技术.
  • 基金资助:

    国家自然科学基金资助项目(71071047,71371062,71302014);高等学校博士点基金资助项目(20090111110016,20120111120029)

An Adaptive Group Evaluating Method for Cross-Efficiencies of Decision Making Units

ZHANG Qi-ping1, LIU Ye-zheng2,3, JIANG Yuan-chun2,3   

  1. 1. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China;
    2. School of Management, Hefei University of Technology, Hefei 230009, China;
    3. Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei 230009, China
  • Received:2012-05-03 Revised:2013-02-21 Online:2014-11-20 Published:2014-11-21

摘要: 传统交叉效率评价方法因决策单元偏好权重不唯一而难以操作,因交叉效率有效性分值平均化集结而难以被接受。目前的学者通常围绕决策单元指标权重的确定性分配方法、交叉效率有效性分值的去平均化集结等分别开展研究。本文将交叉效率评价方法中自评互评相结合的评价模式看作群决策过程,即每个决策单元既是一个被评对象,又是一个决策"专家",提出了一种决策单元交叉效率的自适应群评价方法,将决策单元偏好权重的确定和交叉效率有效性分值的去平均化集结作为同一个决策过程,根据每个决策单元的评价结果与群体评价结果的接近程度,同步迭代调整决策单元的"专家"权重和决策单元自评产生的、并提供给其他被评价决策单元的一组确定的偏好指标权重。实验验证与实例运用分析表明,该方法收敛效果良好,能得到客观稳定的决策单元交叉效率有效性分值及排序。

关键词: 数据包络分析, 决策单元, 群评价, 交叉效率, 自适应

Abstract: Traditional cross efficiency evaluation method lacks maneuverability due to that the preferential weight system is always not unique for some or all DMUs, and it lacks acceptability due to using the ultimate average cross efficiency scores to rank all DMUs. Current studies typically target deterministic distribution of index weights and ultimate cross efficiency scores assembling based on elimination of the assumption of average as two independent decision making problems and solve them respectively. In this paper, the evaluation model which combines self-evaluation and peer-evaluation is seen as a group decision making process, in which each DMU is treated as an "expert" and an opinion object simultaneously, and then an adaptive DMUs cross efficiency group evaluating algorithm is proposed. According to the close degree of evaluation results which are from each DMU and DMUs group, the algorithm gets "expert" weight for each DMU and deterministic preferential index weight systems for each DMU, which are used to evaluate themselves and other DMUs, in a single decision making process by iterative adjustments. The experimental verification and empirical research illustrate that algorithm proposed in this paper can efficiently converge, which can get objective and stable ultimate efficiency scores to rank all DMUs deterministically.

Key words: DEA(Data Envelopment Analysis), DMU(Decision Making Units), group evaluating, cross efficiency, adaptive

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