Collaborative Routing Optimization for Heterogeneous Trucks–Drones Under Urban Regional Restrictions

Abstract

Collaborative truck–drone delivery is a crucial model of drone involvement in urban logistics, addressing drone limitations in load capacity and endurance. However, regional constraints, including damage, blockades, pollution, and epidemics, pose routing challenges for trucks and drones. This study integrates regional restrictions into the heterogeneous truck–drone routing problem, presenting a mixed-integer programming model for cost minimization. To tackle complexity, we introduce an enhanced gray wolf optimization algorithm (EGWO), which improves the initial solution through partition scanning and a heuristic insertion algorithm. EGWO effectiveness is confirmed through enhancements in the standard test library. On average, the heterogeneous truck–drone model achieves a 28.31% cost reduction compared to the single-type truck delivery model. Moreover, deep insights into the impacts of multi-type trucks, the number of no-fly zones and the number of restricted traffic zones on the performance of the heterogeneous truck–drone system are discussed.