Title: Stochastic closed-loop supply chain models: literature review, recent developments, and future research directions

Authors: Omar Elfarouk; Kuan Yew Wong; Shamraiz Ahmad

Addresses: School of Mechanical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Malaysia; Mechanical Engineering Department, The British University in Egypt, Elsherouk City, Cairo, Egypt ' School of Mechanical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Malaysia ' School of Mechanical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Malaysia; School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology, 44000 Islamabad, Pakistan

Abstract: A closed-loop supply chain (CLSC) has been defined as a path that the material flows, starting from suppliers till it arrives at customers as a final product, including product recovery from customers to manufacturers for various usages. A stochastic CLSC handles uncertainty in critical parameters that affect CLSC design. This novel study presents a stochastic CLSC review and categorises uncertainty types applied to stochastic parameters under analysis. Also, the study describes various algorithms that are suitable for solving the different stochastic CLSC models. The research benefits practitioners and researchers by creating guidelines for stochastic CLSC design and discusses the strengths and weaknesses of algorithms used. The results showed the significance of a hybrid genetic, particle swarm optimisation (hybrid GA-PSO) in optimising constrained stochastic CLSC models and the advancement of stochastic CLSC research in the automotive industry. Future research should explore more uncertain parameters, methods of modelling social aspects, and new strategies to implement in stochastic CLSC.

Keywords: closed-loop supply chain; CLSC; stochastic CLSC; solution algorithms; modelling techniques; uncertainty types; uncertainty parameters; reverse logistics; hybrid particle swarm optimisation; stochastic CLSC strategies; stochastic CLSC design; constrained CLSC model.

DOI: 10.1504/IJOR.2023.132257

International Journal of Operational Research, 2023 Vol.47 No.3, pp.357 - 383

Received: 13 Dec 2019
Accepted: 08 Dec 2020
Published online: 14 Jul 2023 *

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