Dual-Process Optimization for Multi-Vehicle Route Planning and Parts Collection Sequencing

Abstract

We proposed a novel dual-process optimization approach for parts collection order and route planning in parts warehouses. Conventional multi-agent parts collection typically uses the vehicle routing problem (VRP), which focuses on minimizing the number of agents and costs. However, the model does not fully leverage the vehicle’s potential. Moreover, multi-agent path finding (MAPF) focuses on route planning and avoiding path conflicts, ignoring the order of part collection. The proposed approach integrates algorithms from the traveling salesman problem (TSP) and path planning, and modifies them to suit the dynamic and complex environment of parts warehouses. This integration streamlines the collection process and considerably reduces the operational time. Thus, the study can improve automation and efficiency in parts warehouse management and improve optimization techniques. The proposed method achieved more than tenfold acceleration compared with the ideal centralized optimization, without cost increments. As the number of agents and part collections increases, centralized optimization requires a metaheuristic approach, which results in solution degradation. However, the proposed approach maintains over tenfold acceleration and produces solutions with shorter operational times. Furthermore, we conducted an ablation study comparing six methods, from entirely independent to centralized optimization, demonstrating that the proposed approach effectively balances computational time and solution accuracy.