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Stage efficiency evaluation in a two-stage network data envelopment analysis model with weight priority

机译:重量优先级两级网络数据包络分析模型中的阶段效率评价

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摘要

Conventional DEA models treat the entire production system as a black box and ignore its internal structures. To address this issue, many studies have examined the DEA efficiencies of two-stage systems in which all outputs of the first stage are the only inputs to the second stage. Based on game theory, the non-cooperative model and centralized model were developed for such a two-stage network structure. However, for the centralized model with multiple optimal solutions and the non-cooperative model, an assumption is required as to whether the first or second stage should be assigned the absolute priority for optimization. In many cases, certain circumstances might exist in which one stage does not completely dominate the other stage. In this paper, we develop a methodology for assessing the overall and stage efficiencies by considering the different and DMU-specific degree of priority given to the stages. Particularly, the non-cooperative model and the centralized model can be deemed as special cases. Moreover, we compare the proposed approaches with the existing approaches, which indicates that our approaches can greatly reduce the computational burden. Two empirical examples are used to demonstrate the proposed approach. (C) 2019 Elsevier Ltd. All rights reserved.
机译:传统的DEA模型将整个生产系统视为黑匣子并忽略其内部结构。为了解决这个问题,许多研究已经检查了两级系统的DEA效率,其中第一阶段的所有输出是第二阶段的唯一投入。基于博弈论,开发了非合作模型和集中模型,用于这种两级网络结构。然而,对于具有多个最佳解决方案和非协作模型的集中模型,需要一个假设,以及是否应该为第一个或第二阶段分配绝对优先级以进行优化。在许多情况下,某些情况可能存在其中一个阶段并不完全支配另一个阶段。在本文中,我们通过考虑阶段的不同和DMU特定程度来制定一种评估整体和阶段效率的方法。特别是,非协作模型和集中模型可以被视为特殊情况。此外,我们将提出的方法与现有方法进行比较,这表明我们的方法可以大大降低计算负担。使用两个经验例子来证明所提出的方法。 (c)2019 Elsevier Ltd.保留所有权利。

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