Abstract

In this thesis we introduce the Multiple Drone and Truck Arc Routing Problem (MDTARP) which considers a fleet of drones used in synchronization with a ground vehicle to provide service to edges in a network within a given time-horizon. Drones launch from a ground vehicle to perform services on a set of edges and return to recharge batteries ready for its next trip. We formulate a multi-objective mathematical model that maximizes coverage of edges on a network based on given weights while minimizing unnecessary travel by drones and the ground vehicle. We develop an Iterated Local Search heuristic and a Cluster-based Location Search heuristic and assess their performance on several instances representing different scenarios. We compare results with solutions obtained using a commercial solver to showcase the effectiveness of the heuristics and perform computational experiments to provide recommendations for the users.