Optimizing Reverse Logistics: A Multi-Objective Approach for Sustainable Inventory Routing problem
Résumé
This study addresses the optimization of reverse logistics within the framework of the circular economy by focusing on the Inventory Routing Problem with Pickup and Delivery and Time Windows (IRP-PD-TW). While forward logistics systems have been extensively explored, the reverse flow of goods—central to sustainable and circular supply chains—remains underdeveloped. This research proposes a bi-objective mathematical model that integrates both economic and social dimensions by simultaneously minimizing total logistics costs (including transport and inventory holding) and improving driver well-being through reduced fatigue and scheduled rest periods. Two solution methodologies are applied: lexicographic optimization, which prioritizes objectives based on predefined hierarchies, and the Non-dominated Sorting Genetic Algorithm II (NSGA-II), used to generate Pareto-efficient solutions. Numerical experiments conducted on benchmark instances show that the proposed exact method outperforms metaheuristic approaches in both cost efficiency and driver well-being. The results validate the model's ability to support sustainable decision-making in closed-loop logistics systems. This work contributes to the development of holistic reverse logistics strategies, offering a practical and socially responsible framework for supply chain optimization. Future research directions include extending the model to dynamic and large-scale networks and integrating emerging technologies such as blockchain and IoT to enhance traceability, efficiency, and compliance with sustainability objectives.
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