Synchronized truck-drone routing with adaptive fleet planning for heterogeneous monitoring tasks

Truck-drone synchronization systems offer a promising solution for various monitoring tasks. However, existing studies typically assume homogeneous task characteristics and adopt predefined truck-drone fleet configurations with fixed assignments of drones to trucks. Such assumptions may not accurately reflect real-world conditions, where heterogeneous monitoring tasks are widespread and often require different types of drones equipped with specific functional devices. In practice, overlooking this heterogeneity can lead to redundant logistics operations and diminished system efficiency. To address this limitation, we develop a mixed integer programming (MIP) model for the truck-drone synchronization system that incorporates heterogeneous monitoring tasks. The proposed model jointly optimizes two key decisions to minimize the total operational cost: deciding the types and numbers of drones assigned to each truck, and determining the routes for both trucks and drones to complete heterogeneous tasks. To solve large-scale instances efficiently, a customized Adaptive Large Neighborhood Search (ALNS) algorithm with problem-specific operators is developed. Numerical experiments show that the proposed model not only effectively accommodates task heterogeneity but also outperforms classical approaches based on predefined truck-drone fleet configurations. Additionally, the ALNS algorithm exhibits satisfactory computational efficiency in producing near-optimal solutions for large-scale instances.