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Variations effect of intermediate products on the second stage in two-stage processes

机译:中间产品在两阶段过程中对第二阶段的变化影响

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Network data envelopment analysis (NDEA) deals with evaluating the performance of a set of homogeneous production processes (or decision making units (DMUs)) taking into account the internal structure of DMUs. A large number of studies in NDEA are based on two-stage structures with intermediate products. For a two-stage DMU, if the intermediate products increase by increasing the inputs of the first stage, then increasing of the intermediate products may lead to some variations on the outputs of the second stage. In this research, this issue is called "variations effect" of the intermediate products on the outputs of the second stage. These variations are very significant for a decision maker from the economic viewpoint because, in this case, he/she can identify variations of the outputs of the second stage and also recognize congestion by increasing the intermediate products; and then decides whether increasing the intermediate products is good or not for improving the performance of the two-stage DMU. This fact is the motivation for creating this study which hitherto, none of the studies in NDEA pay attention to this issue. The current research with the aim of tackling this issue proposes a DEA approach to determine the range of variations of the intermediate products and specify the effect of these variations on the outputs of the second stage in the presence of both negative and non-negative data. Finally, two numerical and empirical examples are provided to illustrate the potential application of the proposed approach. (C) 2018 Elsevier Ltd. All rights reserved.
机译:网络数据包络分析(NDEA)考虑到DMU的内部结构,评估一组同类生产过程(或决策单元(DMU))的性能。 NDEA中的大量研究都是基于带有中间产品的两阶段结构。对于两阶段DMU,如果中间产品通过增加第一阶段的投入而增加,则中间产品的增加可能会导致第二阶段的输出产生某些变化。在本研究中,此问题被称为中间产品对第二阶段输出的“变异效应”。从经济角度来看,这些变化对于决策者而言非常重要,因为在这种情况下,他/她可以识别第二阶段输出的变化,并通过增加中间产品来识别拥塞。然后决定增加中间产品对于改善两阶段DMU的性能是否有利。迄今为止,这一事实是创建此研究的动机,NDEA的研究均未关注此问题。为了解决该问题,当前的研究提出了一种DEA方法,以确定中间产品的变化范围,并在存在负数据和非负数据的情况下,指定这些变化对第二阶段输出的影响。最后,提供了两个数值和经验示例来说明该方法的潜在应用。 (C)2018 Elsevier Ltd.保留所有权利。

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