A multi-objective optimization modelling for design and planning a robust closed-loop supply chain network under supplying disruption due to crises

Global crises such as COVID-19 pandemic and the Russian-Ukrainian war pose many challenges for closed-loop supply chain networks (CLSCN) due to the lack of supplies of raw materials and returned products. Therefore, this research focused on developing a multi-objective MILP mathematical model for the design and planning of CLSCN to help overcome these challenges considering the uncertainty in both the supplying capacity of the raw materials and the return rate of the used products.The developed models aim to maximize total profit, minimize total cost, and maximize overall cus-tomer service level (OCSL) using the e-lexicographic procedure.The effect of variation in both the supply capacity and return rate of the used products on the design and performance of the CLSCN have been studied. It is recommended to optimize the profit then the total cost with a maximum allowable deviation of 5%, and finally optimize the OCSL.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams Uni-versity. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/ by-nc-nd/4.0/).

Automated summary

This research aimed to develop a multi-objective MILP mathematical model for the design and planning of closed-loop supply chain networks (CLSCN) in order to tackle the challenges posed by global crises such as COVID-19 pandemic and the Russian-Ukrainian war. The model considered the uncertainty in both the supplying capacity of raw materials and the return rate of used products, with the goal of maximizing total profit, minimizing total cost, and maximizing overall customer service level (OCSL) using the e-lexicographic procedure.