权力不对称双梯队供应链效率评价的不确定网络DEA模型

IF 1.9 4区 数学 Q1 MATHEMATICS
Bao Jiang, L. Peng, Jian Li, Waichon Lio
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引用次数: 0

摘要

如今,绿色和可持续供应链中出现的一些新兴问题面临越来越多的解决方案,包括不确定因素和未观察到的数据,这使得评估供应链效率变得更加复杂。为了解决这一问题,本文将不确定性理论应用于两阶段网络DEA,旨在处理不准确的数据。此外,双梯级供应链在参与成员之间普遍存在权力不对称的问题。因此,本文分别提出了第一阶段和第二阶段的流失率(D)和履约率(G),以揭示不同领导-追随者模式下的双梯队供应链。前两个模型假设上游企业是领导者,后两个模型假设下游企业是领导者。通过算例分析发现,在不同的模式下,评价结果也会发生相应的变化。采用所提出的模型可以帮助多阶段供应链的决策者更有效地做出决策,避免可能出现的错误。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Uncertain network DEA models for evaluating efficiencies of Bi-echelon supply chain with asymmetric power
Nowadays, some burgeoning issues occurring in green and sustainable supply chain face an increasing number of tackles,  including uncertain factors and unobserved data, which makes it even more complicated to assess supply chain efficiency. To address this issue, this paper applies uncertainty theory to two-stage network DEA  intending to deal with inaccurate data. Moreover, the bi-echelon supply chain generally suffers asymmetric power among involved members. Therefore, this paper proposes attrition rate (D) and fulfillment rate (G) in the first and second stages respectively so as to reveal the  bi-echelon supply chain within different leader-follower patterns. The first two models assume the upstream firm is the leader while the last two models regard the downstream firm as the leader. We find out that the results of the evaluation vary under divergent patterns accordingly by running a numerical example. Adopting proposed models can help decision-makers of the multistage supply chain make decisions more effectively and avoid possible mistakes.
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来源期刊
CiteScore
3.50
自引率
16.70%
发文量
0
期刊介绍: The two-monthly Iranian Journal of Fuzzy Systems (IJFS) aims to provide an international forum for refereed original research works in the theory and applications of fuzzy sets and systems in the areas of foundations, pure mathematics, artificial intelligence, control, robotics, data analysis, data mining, decision making, finance and management, information systems, operations research, pattern recognition and image processing, soft computing and uncertainty modeling. Manuscripts submitted to the IJFS must be original unpublished work and should not be in consideration for publication elsewhere.
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