Multi-objective optimisation of sustainable closed-loop supply chain networks in the tire industry

Reza Kiani Mavi, Seyed Ashkan Hosseini Shekarabi, Neda Kiani Mavi, Sobhan Arisian, Reza Moghdani

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

As environmental concerns and social legislation continue to gain importance, supply chain decision-makers are increasingly required to consider economic and ecological objectives. A potential strategy for mitigating sustainability issues entails the utilisation of discarded tyres through the process of recycling. Nevertheless, the establishment of a closed-loop supply chain that is both sustainable and profitable presents a noteworthy challenge. This study proposes a novel multi-objective mixed-integer linear programming model to design a sustainable closed-loop supply chain network in the tire industry. The objective of the model is to optimize the overall cost of the network, taking into account the environmental consequences related to the establishment of facilities, tire processing, and transportation. While metaheuristic algorithms have been extensively employed to solve network design problems, they are not very effective in handling large-scale networks. To overcome this limitation, our study introduces six new multi-objective evolutionary algorithms based on decomposition (MOEA/D) variants. The present study introduces a prospective methodology for devising supply chain networks that are sustainable in nature, while simultaneously ensuring a harmonious equilibrium between economic and environmental considerations. The efficacy of the proposed multi-objective mixed-integer linear programming model and its MOEA/D variants in addressing large-scale networks has been demonstrated through the obtained results. As such, this study contributes to sustainable supply chain management, which is becoming increasingly important in the current environment.

Original languageEnglish
Article number107116
Number of pages22
JournalEngineering Applications of Artificial Intelligence
Volume126
Issue numberPart D
Early online date27 Sep 2023
DOIs
Publication statusPublished - 1 Nov 2023

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