Designing a Tri-Objective, Sustainable, Closed-Loop, and Multi-Echelon Supply Chain During the COVID-19 and Lockdowns

IF 1.8 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Sina Abbasi, Maryam Daneshmand-Mehr, Armin Ghane Kanafi
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引用次数: 31

Abstract

Abstract This paper proposes a mathematical model of Sustainable Closed-Loop Supply Chain Networks (SCLSCNs). When an outbreak occurs, environmental, economic, and social aspects can be traded off. A novelty aspect of this paper is its emphasis on hygiene costs. As well as healthcare education, prevention, and control of COVID-19, this model offers job opportunities related to COVID-19 pandemic. COVID-19 damages lead to lost days each year, which is one of the negative social aspects of this model. COVID-19 was associated with two environmental novelties in this study. positive and negative effects of COVID-19 can be observed in the environmental context. As a result, there has been an increase in medical waste disposal and plastic waste disposal. Multi-objective mathematical modeling whit Weighted Tchebycheff method scalarization. In this process, the software Lingo is used. The COVID-19 pandemic still has a lot of research gaps because it’s a new disease. An SC model that is sustainable and hygienic will be developed to fill this gap in the COVID-19 condition disaster. Our new indicator of sustainability is demonstrated using a mixed-integer programming model with COVID-19-related issues in a Closed-Loop Supply Chain (CLSC) overview.
构建新冠肺炎疫情防控期间三目标、可持续、闭环、多级供应链
摘要本文提出了可持续闭环供应链网络的数学模型。当疫情发生时,可以权衡环境、经济和社会方面的因素。这篇论文的新颖之处在于它对卫生成本的强调。除了健康教育和防控外,该模式还提供了与COVID-19大流行相关的就业机会。COVID-19造成的损失导致每年损失的天数,这是这种模式的负面社会影响之一。在这项研究中,COVID-19与两个环境新事物有关。在环境背景下,可以观察到COVID-19的积极和消极影响。因此,医疗废物和塑料废物的处理有所增加。基于加权Tchebycheff方法的多目标数学建模。在这个过程中,使用到Lingo软件。COVID-19大流行仍然有很多研究空白,因为它是一种新疾病。将开发一种可持续和卫生的SC模式,以填补COVID-19条件灾难中的这一空白。我们的新可持续性指标在闭环供应链(CLSC)概述中使用带有covid -19相关问题的混合整数规划模型进行了演示。
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
Foundations of Computing and Decision Sciences
Foundations of Computing and Decision Sciences COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
2.20
自引率
9.10%
发文量
16
审稿时长
29 weeks
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