A hybrid metaheuristic algorithm for data driven leagile sustainable closed-loop supply chain modeling under disruption risk

Abstract. In today's world, production and distribution of products in supply chain systems should be done with careful consideration of the environmental and social issues as global concerns about the emission of greenhouse gases within the manufacturing processes and overlooking the major needs of the public are rising. In this regard, the present paper proposes a new multi-objective model for the Closed-Loop Supply Chain (CLSC) problems by incorporating lot sizing and considering lean, agility, and sustainability factors, simultaneously. Furthermore, a robust possibilistic programming approach was applied for handling the uncertainty of the model. To increase the responsiveness of the system, a Fuzzy C-means Clustering Method (FCCM) was employed in order to select the potential locations based on the proximity to local customers. A new hybrid metaheuristic algorithm was developed in order to improve effciency of the model in dealing with large-size problems and assess the impact of using a single-based initial solution as the income for the second phase of the proposed hybrid algorithm. In addition, to ensure effectiveness of the proposed algorithm, another well-known metaheuristic algorithm was developed. The results achieved by experiments on different test problems approved the superiority of the hybrid metaheuristic algorithm in achieving proper solutions.