Efficient Platooning for Urban Last Mile Logistics Using Multi-Objective Optimization: Performance Comparison and Load Strategies

Urban logistics face significant challenges due to increasing traffic congestion, environmental concerns, and the growing demand for efficient goods transportation within cities. Platooning, a concept by which multiple vehicles travel in close proximity to each other, has emerged as a promising solution to effectively tackle some of the major challenges of urban last mile distribution. However, the coordination of the vehicles that are part of the platoon and the optimal routing of each of the vehicles once they leave the platoon is not a trivial task, so they are usually addressed separately. In this work we propose a novel platoon planning and routing approach specifically designed for urban logistics, which aims to reduce fuel consumption and minimize emissions. We first formulate this scenario mathematically as a multi-objective combinatorial optimization problem driven by two conflicting objectives: the shared convoy time and the total travel time to complete the delivery service. Departing from this problem, we aim to elucidate which is the best cargo assignment strategy, as well as the best performing optimization algorithm to solve the formulated multi-objective problem. Results obtained over a case study in the urban area of Bilbao (Spain) show that including the load assignment problem within the optimization problem has advantages over more rigid algorithmic approaches, while from the algorithmic perspective, the NSGA3 evolutionary metaheuristic reaches slightly superior Pareto quality metrics than other solvers.