Last mile delivery routing problem using autonomous electric vehicles

This paper presents a study on the application of Autonomous Delivery Vehicles (ADVs) in last-mile delivery for urban logistics. Specifically, we focus on a routing problem using multi-stop ADVs, with the goal of minimizing route and vehicle usage costs while satisfying several constraints associated with load and battery capacities, maximum route duration for ADVs, and maximum walking distance for customers. We refer to this problem as the Autonomous Delivery Vehicle Routing Problem (ADVRP) and present its mixed-integer linear programming formulation. Due to the NP-hardness of the problem, we propose a two-phase metaheuristic approach that first clusters customers and determines stopping locations for the ADVs, followed by a phase that determines the optimal routes for the ADVs using hybrid variable neighborhood search and simulated annealing. To evaluate our proposed solution methodology, we conduct computational experiments on various related Vehicle Routing Problems (VRPs) from the literature and newly generated ADVRP instances. The results show that the proposed two-phase metaheuristic approach can produce high-quality solutions with minimal computational effort while outperforming an exact solver in 26 medium-and large-sized instances of ADVRP and reaching optimal solutions in most VRP instances and related problems. Furthermore, we conduct sensitivity analyses on selected problem parameters and present a case study in Istanbul, Turkey to provide managerial insights for implementing ADVs in urban logistics.