Routing and Scheduling of Fresh Product Distribution for Hybrid Truck–Drone Delivery with Temporary Drone Stations

Abstract

To save costs and increase profit in the last mile of fresh product distribution, considering the distribution characteristics of urban customers, the perishability of fresh products, and the cooperative mode of trucks and drones, a temporary drone station is introduced into a traditional logistics network, and the routing and scheduling problem of hybrid truck–drone cooperative delivery is proposed. First, a multi-objective mixed-integer linear programming model is constructed to minimize the total operating cost and maximize customer satisfaction. Second, a two-layer programming solution method based on density-based spatial clustering of applications with noise (DBSCAN) and multi-objective genetic algorithm with cooperative strategy (MOGA-CS) is designed to solve this problem. Moreover, DBSCAN is used to determine the location of the temporary drone station. MOGA-CS embeds a collaborative evolutionary strategy with two populations under the framework of non-dominated sorting genetic algorithm II (NSGA-II) and incorporates local search to solve the path optimization problem of truck–drone cooperative distribution. Finally, the superiority of the hybrid distribution mode proposed in this paper is verified by numerical experiments, and the efficiency of MOGA-CS is further verified by comparing it with NSGA-II, multi-objective differential evolution (MODE), and non-dominated sorting whale optimization algorithm (NSWOA).