Previously used exclusively in military domain, unmanned aerial vehicles (drones) have stepped up to become a part of new logistic method in commercial sector called "last-mile delivery". In this novel approach, small unmanned aerial vehicles (UAV), also known as drones, are deployed alongside with trucks to deliver goods to customers in order to improve the service quality and reduce the transportation cost. It gives rise to a new variant of the traveling salesman problem (TSP), called TSP with drone (TSP-D). A variant of the problem which aims to minimize the time at which the last customer is serviced (or to maximize the service quality in other words) was studied in the literature [1] . This article considers a new variant of the TSP-D where the objective is to minimize the total transportation cost. The problem is first formulated mathematically and two algorithms are proposed for the solution. The first algorithm (TSP-LS) inspired from the idea of [1] in which an optimal TSP solution is converted to a feasible TSP-D solution by local searches. The second one, a Greedy Randomized Adaptive Search Procedure (GRASP), is based on a new split procedure which optimally splits any TSP tour into a TSP-D solution. Once a TSP-D solution is generated, it is then improved through local search operators. Numerical results obtained on various instances with different sizes and characteristics are presented.
Ha et al. REF considered a variant of the FSTSP that aims to minimize operational costs.
On the Min-cost Traveling Salesman Problem with Drone
ArXiv
ArXiv
Engineering Computer Science