Optimizing an On-Demand Delivery Mode Based on Trucks and Drones

We explore a novel on-demand delivery mode based on cooperation between trucks and drones. A fleet of trucks, each of which carries several drones, travels along a closed-loop route, and the drones are launched from the trucks to pick up (or deliver) ordered parcels from their origin (or to their destination). The fulfillment of an order (i.e., delivering the parcel from its origin to its destination) includes three steps: pick up by a drone, transport by a truck, and delivery by a drone. We investigate how to fulfill all of the orders in one batch in order to minimize the total operational cost. We build a mixed-integer programming (MIP) model for this new on-demand delivery system in a network of multiple routes with transshipment. For drones, the assignment decision regarding the fulfillment stages for the orders and the location decision regarding the launching from and landing onto trucks are optimized by the proposed MIP model. An exact branch-and-price algorithm is designed to efficiently solve the model on large-scale instances. We validate the advantages of our algorithm in terms of computing time and solution quality through experiments on both artificial and real data. We validate the benefits of both implementing this new delivery mode and allowing transshipments among routes using a drone to serve multiple orders in one flying trip and consolidating orders. We also investigate the influences of the number of drones, speed, endurance time, unit penalty cost, and the geographic distribution of orders on the system’s operational cost. Funding: This research was supported by the National Natural Science Foundation of China Grants 72025103, 72394360, 72394362, 72361137001, and 7237122; the China Postdoctoral Science Foundation Grant 2024M761921; the Project of Science and Technology Commission of Shanghai Municipality China Grant 23JC1402200; and the Research Grants Council of the Hong Kong Special Administrative Region, China Grant HKSAR RGC TRS T32-707/22-N. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0693 .