Routing of Hybrid Truck-Drone Delivery Systems: Mathematical Models and Solution Approaches (March 2023)

PhD dissertation final oral defense presentation by PhD-ESM candidate Batool Mezar Madani

With the continuous growth of e-commerce, the responsibility of logistics providers for solving last mile delivery challenges is surging. The last mile delivery is the most polluting, most expensive and least efficient part of the e-commerce supply chain. Therefore, logistics providers seek new solutions to improve delivery operations continuously.

Technological advances in drones have paved the way for several applications in the field of logistics. However, restrictions such as battery and payload capacity have restricted the effective operational use of drones in many practical applications. To assist toward alleviating these operational limitations, several hybrid delivery models incorporating one or more drones launched from a larger vehicle have emerged. A recent research avenue is to integrate drones with traditional delivery methods such as trucks to form a hybrid truck-drone delivery system. The hybrid truck-drone delivery system has received significant attention in the literature, due to its new operational challenges and the interdependency between the vehicles and their roles. Most of the literature considers a single customer visit per drone dispatch while launching and collecting the drone from customer nodes visited by the truck. In this research, three HTDDS models with drone multi-visits are introduced where the first model investigates the routing considerations of a truck acting as a moving depot for launching and collecting a drone at non-customer nodes. The second model permits flexibility in the drone’s launching and collecting operations by utilizing customer and non-customer nodes. The final model studies the extended version of the second model by employing several trucks in the delivery system as well as considering time-related constraints. For handling large instances of the proposed models, efficient solution approaches with novel strategies are introduced. In particular, a modified saving algorithm with neighborhood reduction schemes and variable neighborhood search frameworks with different neighborhood selection strategies are developed. In addition, a generalized nomenclature for defining the neighborhood structures is established, which can be applied to a variety of routing problems. Extensive computational analysis is performed to validate and test the performance of the proposed solution methods. Numerical findings indicate that the proposed solution methods outperform the basic approaches.

PhD supervisor: Dr. Malick Ndiaye, Department of Industrial Engineering