An Agent-Based Simulation and Optimization Approach for Sustainable Urban Logistics: A Case Study in Lisbon

Abstract

Urban logistics plays a crucial role in ensuring the efficient movement of goods in densely populated areas. This study examines the PDP-TW in an urban logistics context using an integrated approach that combines an agent-based simulation model and an optimization model. The research focuses on a real-world case study, comparing the company’s current operational scenario with an optimized scenario generated through a PDP-TW model adapted from the literature. The findings reveal that the optimized model reduced the total distance traveled by approximately 38%, while the simulated optimized scenario achieved a reduction of about 36.5%. Consequently, the total cost decreased from EUR 116.50 in the real-world operations to EUR 71.21 in the optimization model and EUR 73.29 in the simulated optimal real scenario. Additionally, the optimized approach required only two drivers instead of three, indicating potential efficiency gains in resource allocation. In the optimization model, window constraints were strictly satisfied. However, in the agent-based simulation, a few deliveries were completed within the 10 min empirical tolerance threshold, rather than within the scheduled window itself. This outcome underscores the need for enhanced scheduling strategies to increase time window robustness under real-world execution variability. Despite these advancements, the ABS model remains deterministic and does not account for uncertainties such as traffic congestion or vehicle breakdowns. Future work should incorporate stochastic elements and evaluate the model’s scalability with a larger dataset and instances to better understand its applicability in real-world logistics operations.