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FAIR RANKING OF THE DECISION MAKING UNITS USING OPTIMISTIC AND PESSIMISTIC WEIGHTS IN DATA ENVELOPMENT ANALYSIS

机译:数据包络分析中使用最优权重和最优权重的决策机构公平排名

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Ranking all of the decision making units (DMUs) is one of the most important topics in Data envelopment analysis (DEA). Provided methods for ranking often rank the efficient units. Ranking inefficient units by early DEA models has some weaknesses since slacks are ignored. One of the methods presented in the ranking of all DMUs is Khodabakhshi and Ariavash's method [M. Khodabakhshi and K. Ariavash, Appl. Math. Lett. 25 (2012) 2066-2070.] in this method, the maximum and minimum efficiency values of each DMU are measured by considering the sum of all efficiencies equal one. Finally, the rank of each DMU is determined in proportion to a convex combination of its minimum and maximum efficiency values. But optimistic and pessimistic weights of the other DMUs are not considered in ranking of the evaluated DMU. In this paper, a fair method to rank all DMUs, using Khodabakhshi and Ariavash's method is proposed. In the proposed method optimistic and pessimistic efficiency values will be assessed, not only by the optimal weights of evaluated DMU but also by considering the optimistic and pessimistic optimal weights of all DMUs. The obtained optimistic and pessimistic efficiency values are supposed as criterion for the ranking. The proposed method is illustrated by a numerical example.
机译:对所有决策单位(DMU)进行排名是数据包络分析(DEA)中最重要的主题之一。提供的排名方法经常对有效单位进行排名。早期的DEA模型对低效单位进行排名存在一些缺点,因为忽略了松弛。在所有DMU的排名中介绍的一种方法是Khodabakhshi和Ariavash的方法[M. Khodabakhshi和K.Ariavash,应用数学。来吧25(2012)2066-2070。]中,通过考虑所有效率之和等于1来测量每个DMU的最大效率值和最小效率值。最后,每个DMU的等级取决于其最小和最大效率值的凸组合。但是,在评估的DMU的排名中,未考虑其他DMU的乐观和悲观权重。本文提出了一种使用Khodabakhshi和Ariavash方法对所有DMU进行排序的公平方法。在提出的方法中,不仅通过评估的DMU的最佳权重,而且通过考虑所有DMU的乐观和悲观的最优权重,可以评估乐观和悲观的效率值。所获得的乐观和悲观效率值被认为是排名的标准。数值例子说明了所提出的方法。

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