The sustainable reverse inventory-routing problem with time windows and simultaneous pickup and delivery

Circular economy principles are increasingly central to sustainable supply chain management, as they foster closed-loop systems that reduce waste, extend product lifecycles, and promote resource efficiency. In particular, they have strong implications for the social dimension, since reverse logistics and reuse systems directly influence working conditions. Optimizing transport operations is crucial for minimizing environmental impact, reduce operational costs and enhance social outcomes such as operator well-being. This paper addresses the Inventory Routing Problem with Pickup and Delivery with time window (IRP-PD-TW) in the context of pallet logistics, focusing on the distribution of loaded pallets and the collection of empty ones. The problem we tackle lies in the lack of integrated optimization approaches that jointly consider economic, environmental, and social criteria within this context. To address this gap, we propose a multi-objective Mixed Integer Linear Programming (MILP) model designed to optimize multiple objectives: minimizing total costs, reducing CO2 emissions by accounting for both load and distance factors, and improving driver welfare. The model is implemented and solved using Gurobi, with validation conducted on literature instances. Additionally, the problem is tackled using two metaheuristic approaches, namely NSGA-II and SPEA2, which leverage Pareto optimization to explore trade-offs among the objectives. The results demonstrate the effectiveness of both methods in achieving balanced optimization of economic, environmental, and social objectives, thus offering a comprehensive approach to sustainable supply chain.