Sustainable last mile parcel delivery and return service using drones

Abstract

Improving last mile delivery, in terms of both efficiency and sustainability, has recently been enhanced using truck/drone tandems. However, nearly all these approaches focus only on deliveries, while returns are ignored. We propose a new model for integrating both delivery and returns in the combined operation of a truck and a drone, which we term the Flying Sidekick Traveling Salesman Problem integrating Deliveries and Returns with Multiple Payloads (FSTSP-DR-MP). This approach is more sustainable than truck-only deliveries as the drones operate through battery power with no emissions and can move more directly than vehicles along a road network. We formulate the problem as a mixed-integer linear program to minimize the total service time of the system, where the truck and the drone can perform both delivery and pickup of parcels during a single sortie. Because drones have a capacity of multiple parcels, each can visit more than one customer per dispatch, increasing drone utilization and responsiveness to customer expectations regarding return services, along with greater environmental improvements. Small-size cases are solved exactly using the MILP implemented in the CPLEX Python API. Since the MILP is not practical for realistically sized cases, we propose a meta-heuristic derived from Variable Neighborhood Search (VNS), which iteratively builds truck and drone routes. We show that this works well for up to 100 customers, a typical number in last-mile logistics. We assess the trade-offs between the ratio of returns to deliveries and drone capacity to provide managerial insights to the benefits of this approach for sustainable last mile logistics. Computational experiments show that integrating delivery and return truck-drone operations significantly reduces total service time and truck travel time compared to both traditional delivery schemes (single truck) and the well-known FSTSP drone schemes (one truck and one drone) up to 36.2% and 22.9%, respectively, by exploiting the nature of each customer (delivery or return) to increase the number of stops of each drone sortie. We also conduct a comparison with multiple drones have a single payload under similar scenarios. The results demonstrate that our model outperforms this alternative with an average improvement of 3.9% in total service time. Our approach addresses an important gap in the literature by accommodating returns, which are ubiquitous in last mile logistics, as well as providing for improved drone utilization, sustainability, and cost-effectiveness.