An adaptive hybrid neighborhood search algorithm for the electric vehicle pickup and delivery problem with time windows and partial charging

Environmental pressures and measures are compelling the extensive integration of electric vehicles into transportation and logistics systems. This paper focuses on addressing the electric vehicle pickup and delivery problem with time windows and partial charging, in which the amount of charging electricity at charging stations is flexible and determined based on the route schedules. A new effective mixed-integer linear programming model has been developed for the problem. To effectively tackle large-scale instances, we propose an adaptive hybrid neighborhood search algorithm, which is based on the framework of the adaptive large neighborhood search algorithm. The proposed algorithm incorporates various problem-oriented search operators being adaptively chosen for evolution. Meanwhile, dynamic programming-based charging approaches for both full and partial charging policies are presented. Numerical experiments are conducted using benchmark instances of the electric vehicle pickup and delivery problem to verify the effectiveness of our algorithm configurations and its overall performance. The solution results are compared against those obtained using the state-of-the-art algorithm, and the proposed algorithm identifies 21 new best solutions and exhibits greater stability, which demonstrates the competitiveness of the proposed algorithm. Furthermore, the analysis of charging policies provides interesting insights, highlighting the significant advantage of the partial charging policy in scenarios characterized by clustered customer distributions or short scheduling horizons.