A customized multi-neighborhood search algorithm using the tabu list for a sustainable closed-loop supply chain network under uncertainty.

Environmental pollution and social inequalities have prompted the agricultural industry to implement sustainable supply chain management practices. However, supply chain operations and planning in developing countries such as Iran are often not economically, environmentally, and socially sustainable. To address this challenge, this paper presents a practical optimization model for sustainable closed-loop supply chain (SCLSC) management in the agricultural industry of Iran, with a focus on the olive crop. Based on the triple bottom line concept, which considers economic, environmental, and social sustainability, the proposed model makes location, allocation, and inventory decisions under uncertainty by developing a scenario-based robust optimization model. To solve this complex network design problem, we propose a metaheuristic algorithm with a multi-neighborhood procedure that efficiently handles the complexity of the problem. Specifically, we develop a customized Simulated Annealing (SA) algorithm using a tabu list to improve the initial solution found by a constructive heuristic algorithm. Our extensive analysis and comparison of our metaheuristic algorithm against the exact solver and two other powerful metaheuristic algorithms demonstrate the applicability of our SCLSC model for the agricultural industry in Iran and the high performance of the proposed metaheuristic algorithm for solving large-scale networks. [Display omitted] • A closed-loop supply chain network design problem for the agricultural industry is proposed. • The proposed problem minimizes the expected total cost while considering environmental and social criteria in constraints. • A scenario-based robust optimization approach is used to formulate the proposed problem. • An initial solution for the proposed problem is found by a constructive heuristic algorithm. • A multi-neighborhood search using the tabu list is customized for the proposed model. [ABSTRACT FROM AUTHOR]