Performance analysis in a stochastic supply chain with reverse flows: a DEA-based approach

IF 1.9 3区工程技术 Q3 MANAGEMENT

IMA Journal of Management Mathematics Pub Date : 2021-06-12 DOI:10.1093/IMAMAN/DPAB018

A. Amirteimoori, Leila Khoshandam, S. Kordrostami, M. J. S. Noveiri, R. Matin

引用次数: 0

Abstract

Traditional efficiency studies on network data envelopment analysis (DEA) consider decision-making units as black boxes that use a set of crisp inputs to produce a set of crisp outputs and that ignore intermediate measures and reverse flows. In real applications, however, we are faced with network systems with reverse flows in an uncertain environment. In this paper, therefore, a chance-constrained multistage DEA model is introduced to analyze the relative performances of supply chains and components in the presence of reverse flows and random factors. A real case in the sugar illustrates the proposed method. The results demonstrate the validity and applicability of the model.

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逆向流动随机供应链的绩效分析：一种基于DEA的方法

关于网络数据包络分析（DEA）的传统效率研究将决策单元视为黑匣子，使用一组清晰的输入来产生一组简洁的输出，而忽略中间度量和逆向流动。然而，在实际应用中，我们面临着在不确定环境中具有反向流的网络系统。因此，本文引入了一个机会约束的多级DEA模型来分析在逆向流动和随机因素存在的情况下供应链和组件的相对性能。糖中的一个实际案例说明了所提出的方法。结果证明了该模型的有效性和适用性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IMA Journal of Management Mathematics OPERATIONS RESEARCH & MANAGEMENT SCIENCE-MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

CiteScore

4.70

自引率

17.60%

发文量

审稿时长

>12 weeks

期刊介绍： The mission of this quarterly journal is to publish mathematical research of the highest quality, impact and relevance that can be directly utilised or have demonstrable potential to be employed by managers in profit, not-for-profit, third party and governmental/public organisations to improve their practices. Thus the research must be quantitative and of the highest quality if it is to be published in the journal. Furthermore, the outcome of the research must be ultimately useful for managers. The journal also publishes novel meta-analyses of the literature, reviews of the "state-of-the art" in a manner that provides new insight, and genuine applications of mathematics to real-world problems in the form of case studies. The journal welcomes papers dealing with topics in Operational Research and Management Science, Operations Management, Decision Sciences, Transportation Science, Marketing Science, Analytics, and Financial and Risk Modelling.