Addressing uncertainty in closed-loop supply chain networks: a multi-objective approach to integrated production and transportation problems

IF 1.8 Q3 MANAGEMENT

Journal of Modelling in Management Pub Date : 2024-04-30 DOI:10.1108/jm2-01-2024-0011

Niharika Varshney, Srikant Gupta, Aquil Ahmed

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引用次数: 0

Abstract

Purpose

This study aims to address the inherent uncertainties within closed-loop supply chain (CLSC) networks through the application of a multi-objective approach, specifically focusing on the optimization of integrated production and transportation processes. The primary purpose is to enhance decision-making in supply chain management by formulating a robust multi-objective model.

Design/methodology/approach

In dealing with uncertainty, this study uses Pythagorean fuzzy numbers (PFNs) to effectively represent and quantify uncertainties associated with various parameters within the CLSC network. The proposed model is solved using Pythagorean hesitant fuzzy programming, presenting a comprehensive and innovative methodology designed explicitly for handling uncertainties inherent in CLSC contexts.

Findings

The research findings highlight the effectiveness and reliability of the proposed framework for addressing uncertainties within CLSC networks. Through a comparative analysis with other established approaches, the model demonstrates its robustness, showcasing its potential to make informed and resilient decisions in supply chain management.

Research limitations/implications

This study successfully addressed uncertainty in CLSC networks, providing logistics managers with a robust decision-making framework. Emphasizing the importance of PFNs and Pythagorean hesitant fuzzy programming, the research offered practical insights for optimizing transportation routes and resource allocation. Future research could explore dynamic factors in CLSCs, integrate real-time data and leverage emerging technologies for more agile and sustainable supply chain management.

Originality/value

This research contributes significantly to the field by introducing a novel and comprehensive methodology for managing uncertainty in CLSC networks. The adoption of PFNs and Pythagorean hesitant fuzzy programming offers an original and valuable approach to addressing uncertainties, providing practitioners and decision-makers with insights to make informed and resilient decisions in supply chain management.

查看原文本刊更多论文

解决闭环供应链网络中的不确定性：综合生产和运输问题的多目标方法

目的本研究旨在通过应用多目标方法来解决闭环供应链（CLSC）网络中固有的不确定性问题，特别侧重于综合生产和运输流程的优化。设计/方法/途径在处理不确定性时，本研究使用毕达哥拉斯模糊数（PFN）来有效表示和量化与闭环供应链网络内各种参数相关的不确定性。使用毕达哥拉斯犹豫模糊编程对所提出的模型进行求解，从而提出了一种全面而创新的方法，专门用于处理 CLSC 环境中固有的不确定性。通过与其他既有方法的比较分析，该模型证明了其稳健性，展示了其在供应链管理中做出知情且有弹性决策的潜力。研究局限/影响本研究成功地解决了供应链中心网络中的不确定性问题，为物流经理提供了一个稳健的决策框架。研究强调了 PFN 和毕达哥拉斯犹豫模糊编程的重要性，为优化运输路线和资源分配提供了实用的见解。未来的研究可以探索供应链中心的动态因素，整合实时数据，并利用新兴技术实现更敏捷、更可持续的供应链管理。采用 PFNs 和毕达哥拉斯犹豫模糊编程为解决不确定性问题提供了一种新颖而有价值的方法，为从业人员和决策者在供应链管理中做出明智而有弹性的决策提供了真知灼见。

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

Journal of Modelling in Management MANAGEMENT-

CiteScore

5.50

自引率

12.50%

发文量

期刊介绍： Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.