Agent-Based and Discrete-Event Simulation of Reverse Logistics: A Case Study from the SIIP Project

This paper explores the application of a hybrid simulation methodology—integrating agent-based and discrete-event simulation—to enhance reverse logistics (RL) processes. Reverse logistics is increasingly critical in circular economy strategies, enabling the recovery, remanufacturing, and recycling of end-of-life products while minimizing environmental impacts. Within the context of the Sustainable Intelligent Industrial Planning (SIIP) project, financed by the Italian government, this study develops and applies a multi-method simulation framework to model and evaluate RL supply chains using real-world data provided by an industrial partner. The framework supports the analysis of economic and en vironmental trade-offs across different supply chain configurations, specifically considering direct transportation and transportation to a storage centre before final transportation. Simulation results reveal significant cost-emission trade-offs: while introducing a storage centre reduces CO 2 emissions by optimizing transportation routes, it incurs higher operational costs. The findings emphasize the need for context -specific decision making to balance sustainability goals with economic efficiency, demonstrating the potential of hybrid simulation tools to inform strategic planning in RL systems.