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A Novel Mixed Binary Linear DEA Model for Ranking Decision-Making Units with Preference Information
Computers & Industrial Engineering ( IF 7.9 ) Pub Date : 2020-11-01 , DOI: 10.1016/j.cie.2020.106720
Bohlool Ebrahimi , Madjid Tavana , Mehdi Toloo , Vincent Charles

Abstract Several mixed binary linear programming models have been proposed in the literature to rank decision-making units (DMUs) in data envelopment analysis (DEA). However, some of these models fail to consider the decision-makers’ preferences. We propose a new mixed binary linear DEA model for finding the most efficient DMU by considering the decision-makers’ preferences. The model proposed in this study is motivated by the approach introduced by Toloo and Salahi (2018) . We extend their model by introducing additional assurance region type I (ARI) weight restrictions (WRs) based on the decision-makers’ preferences. We show that direct addition of assurance region type II (ARII) and absolute WRs in traditional DEA models leads to infeasibility and free production problems, and we prove ARI eliminates these problems. We also show our epsilon-free model is less complicated and requires less effort to determine the best efficient unit compared with the existing epsilon-based models in the literature. We provide two real-life applications to show the applicability and exhibit the efficacy of our model.

中文翻译:

一种新的混合二元线性 DEA 模型,用于对具有偏好信息的决策单元进行排序

摘要 文献中提出了几种混合二元线性规划模型来对数据包络分析 (DEA) 中的决策单元 (DMU) 进行排序。然而,其中一些模型没有考虑决策者的偏好。我们提出了一种新的混合二元线性 DEA 模型,通过考虑决策者的偏好来寻找最有效的 DMU。本研究中提出的模型是由 Toloo 和 Salahi (2018) 引入的方法推动的。我们通过基于决策者的偏好引入额外的保证区域 I 型 (ARI) 权重限制 (WR) 来扩展他们的模型。我们表明,在传统 DEA 模型中直接添加保证区域 II (ARII) 和绝对 WR 会导致不可行和自由生产问题,并且我们证明 ARI 消除了这些问题。我们还表明,与文献中现有的基于 epsilon 的模型相比,我们的无 epsilon 模型更简单,并且需要更少的努力来确定最有效的单元。我们提供了两个现实生活中的应用程序来展示我们模型的适用性和功效。
更新日期:2020-11-01
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