Genetic algorithm for scheduling of parcel delivery by drones

Abstract

In recent years, efficient logistics has become indispensable, and using unmanned aerial vehicles (UAVs) or drones is promising for considerably reducing the cost and time required for parcel delivery. This paper addresses a parcel delivery scheduling problem. In this problem, a truck loaded with drones and parcels leaves a distribution center and stops at some points on a fixed route. At each point, the drones take off and deliver parcels to customers. We define this problem as finding the assignment of customers to both the drones and their takeoff points. Then, we propose a genetic algorithm (GA) for finding a near-optimal solution in a short time. In the proposed GA, a solution is represented using sets of customers assigned to the takeoff points, and a heuristic rule determines the assignment to the drones. The crossover operation enables offspring to inherit the customer sets. Experimental results show that the proposed GA can successfully find an optimal or a near-optimal solution faster than an integer programming solver for almost all instances. In addition, it significantly outperforms other GAs using a different crossover.