Optimal routes and aborting strategies of trucks and drones under random attacks

Abstract

This paper is an attempt to explore optimal routes and aborting strategies of trucks and drones under random attacks. Time windows of targets, aborting strategies of trucks and random attacks are considered in the truck-drone routes, which not only fixes the shortcomings of drones, such as its small load and short working hours, but also conducts a preliminary survey to gain disaster or enemy information in the field of emergency management or military. It is assumed that drones and trucks may be attacked in the route and the truck may abort its task according to the number of tasks finished and the number of attacks suffered. In addition, based on a constraint programming model, this paper proposes a hybrid algorithm named ALNS-SP algorithm combined with the adaptive large neighborhood search and the short-path algorithm to solve the vehicle routing problem with trucks and drones considering random attacks problem (VRP-TDA). A test set based on Solomon datasets is designed and comparative numerical experiments are presented to illustrate the performance of the ALNS-SP algorithm.