Designing a sustainable-resilient supply chain network with an emphasis on financing and investment decisions using the NSGA-II algorithm

Document Type : Research Paper

Authors

1 Department of Industrial Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Department of Industrial Management, Faculty of Management, South Tehran Branch, Islamic Azad University, Tehran, Iran

3 Visitor of Science and Research Branch, Islamic Azad University, Tehran, Iran

4 Department of Industrial Management, Faculty of Management, University of Allameh Tabatabai, Tehran, Iran

5 Department of Industrial Management, Faculty of Management, Accounting and Humanities, Qazvin Branch, Islamic Azad University, Qazvin, Iran

10.22075/ijnaa.2023.31030.4547

Abstract

Nowadays, product recovery and waste recycling are receiving more attention in order to reduce environmental pollution and production costs in the form of a closed-loop and sustainable supply chain. Also, designing a supply chain by considering resilience approaches can protect buyers against disruptions such as natural, human or technological disasters. On the other hand, efficient and effective financial supply chain management (FSCM) way is known as one of the main structures in line with the continuity and stability of the chain's performance, and budget restrictions are of great importance considering the issue of scarcity of resources in the economy. This study has presented a multi-objective mixed integer linear programming (MOMILP) model of a single-period, multi-product and multi-level closed-loop supply chain, taking into account the dimensions of sustainability and resilience, emphasizing the balance between the initial budget and the cost of establishing facilities under uncertainty. subsequently, to eliminate the uncertainty of the demand parameters and costs, its robust counterpart was presented based on Pishvaee’s robust possibilistic programming (RPP) model. The augmented Epsilon Constraint method (AEC) and Non-dominated Sorting Genetic Algorithm (NSGA-II) were used to solve and evaluate it.

Keywords

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Articles in Press, Corrected Proof
Available Online from 29 August 2023
  • Receive Date: 22 February 2023
  • Accept Date: 17 July 2023