决策单元交叉效率的自适应群评价方法

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。

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

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.