Ants can solve the parallel drone scheduling traveling salesman problem - Quoc Trung Dinh (GECCO, 2021)
In this work, we are interested in studying the parallel drone scheduling traveling salesman problem (PDSTSP), where deliveries are split between a truck and a fleet of drones. The truck performs a common delivery tour, while the drones are forced to perform back and forth trips between customers and a depot. The objective is to minimize the completion time coming back to the depot of all the vehicles. We present a hybrid ant colony optimization (HACO) metaheuristic to solve the problem. Our algorithm is based on an idea from the literature that represents a PDSTSP solution as a permutation of all customers. And then a dynamic programming is used to decompose the customer sequence into a tour for the truck and trips for the drones. We propose a new dynamic programming combined with other problem-tailored components to efficiently solve the problem. When being tested on benchmark instances from the literature, the HACO algorithm outperforms state-of-the-art algorithms in terms of both running time and solution quality. More remarkably, we find 23 new best known solutions out of 90 instances considered.