Optimizing Drone-Enabled Logistics: Mathematical Models for Regulatory-Constrained Delivery in Urban and Rural Environments

Abstract

The integration of drones into last-mile delivery systems has garnered significant attention across sectors such as e-commerce, healthcare, and emergency logistics. Despite technological advancements and industry investment, widespread adoption remains limited due to regulatory, financial, and operational challenges. This thesis investigates the feasibility and efficiency of drone-assisted delivery under real-world constraints, with a particular focus on regulatory frameworks. Three mathematical models are developed. The first evaluates the impact of no-fly zones and flight distance restrictions on coordinated truck-drone delivery. The second introduces a hybrid logistics model incorporating droneports to facilitate delivery in urban areas with zoning laws. The third addresses a rural healthcare application, proposing a synchronized delivery and scheduling model for livestock vaccine administration, balancing workload among veterinarians. Each model is formulated as a Mixed Integer Linear Program (MILP), with computational experiments validating their performance. Heuristic and matheuristic algorithms are proposed to solve large-scale instances. Results demonstrate that while drones can enhance delivery efficiency and equity, their benefits are significantly influenced by regulatory severity and operational design.