Closed-Loop Supply Chain Network Design Optimization with a Multi-Objective Approach: Leveraging the Red Deer Metaheuristic Algorithm

Abstract

In an era marked by increasing global complexity, the strategic design of a sustainable supply chain has become imperative. Decision-makers are confronted with the challenge of reconciling economic objectives with environmental and social impacts. In this context, gaining a competitive edge hinge on two critical factors: reducing operational costs and elevating service levels. Achieving economic efficiency while adopting a sustainability-oriented approach is the central focus of this research within the closed-loop supply chain domain. This study introduces a comprehensive multi-objective model for supply chain optimization. Distinguishing itself from prior models, our mathematical framework incorporates the social dimension of sustainability into the total cost considerations and accounts for environmental factors. Furthermore, it comprehensively models both forward and reverse flows inherent to closed-loop supply chains. The complexities and diverse objectives within sustainable closed-loop supply chain network design necessitate innovative methodologies. To address this challenge, we introduce the novel Red Deer metaheuristic algorithm, alongside two other cutting-edge algorithms and two conventional ones. Comparative analysis of their performance across problems of varying sizes underscores the remarkable strength and efficacy of the Red Deer algorithm, making it a potent tool for solving complex closed-loop supply chain optimization challenges.