Truck-drone collaborative delivery with a mobile drone station trailer

Truck-drone collaborative delivery systems have garnered significant interest in recent years. This paper introduces a novel model, called the Traveling Salesman Problem with a Mobile Drone Station Trailer (TSP-MDST). The model integrates a truck and multiple drones that collaborate through a Mobile Drone Station Trailer (MDST). The proposed MDST is a trailer transported by a truck and deployed at designated sites to serve as a temporary drone station. Once deployed, thesite is activated, and drones can pick up packages from the MDST to serve customers. While drones are in service, the truck may either remain on-site or continue independently to serve other customers. We formulate the TSP-MDST as a mixed-integer linear programming (MILP) model to minimize the total delivery makespan. To enhance computational efficiency, especially for large-scale instances, we develop a Routing Structure Guided Adaptive Large Neighborhood Search (RSG-ALNS) metaheuristic. The proposed RSG-ALNS is built around a high-level route-structure search and a guided repair procedure under a given structure. The ALNS, which has tailored destroy and repair operators, serves as the underlying neighborhood search framework. Numerical experiments demonstrate the effectiveness of both the MILP formulation and the proposed RSG-ALNS. The RSG-ALNS outperforms both exact and heuristic benchmarks, consistently finding high-quality solutions within reasonable computational time. The algorithm also remains robust for large-scale instances where exact approaches struggle. Comparative analyses against benchmark models highlight the operational advantages of this novel truck-drone collaborative delivery approach, while sensitivity analyses yield managerial insights for practical implementation.