Study on Routing and Scheduling for Unmanned Electric Loaders Considering Charging

Abstract The application of unmanned electric loader technology facilitates safe and efficient enterprise production. By framing the cargo transport from multiple bins to electric loader hoppers as an MDVRPTW-EV issue, this paper proposes the SPBO-ACO hybrid metaheuristic algorithm, which combines the local search capability of the Student Psychology Optimization algorithm with Ant Colony Optimization principles to address path planning challenges. The SPBO-ACO algorithm leverages route length classification, strong and weak perturbations, and learning operators to enhance solution exploration.Testing based on standard MDVRPTW benchmark test instances shows high scalability and stability with 25% of results exceeding or approaching optimal solutions in 20 benchmark cases while 85% have errors compared to optimal solutions that do not exceed 10%. When applied to an industrial setting, the algorithm significantly reduces the unmanned loader's driving distance during raw material feeding and filling, demonstrating its practical effectiveness.