Drone fleet resupply in same-day delivery with fair customer service

Fairness in same-day delivery (SDD) becomes increasingly important as customers demand equitable service pledge, yet cost-driven merchants’ preferences for nearby customers create service disparities across regions. Drone fleet resupply presents a fast and traffic-free solution capable of navigating complex terrains, while truck-based delivery complements it by overcoming the drone’s limitations in endurance and operational range. Previous studies mainly address single-drone, single-truck problems, with limited attention to the potential of drone fleet resupply with multi-truck systems to solve fairness issues. This research fills this gap by originally proposing a drone fleet resupply system for stochastic requests, where drones perform multiple trips to replenish any truck as needed, with consideration of drone endurance limitations. This problem is formulated as a sequential decision process (SDP) and solved through a deep Q-learning (DQL) approach to maximize service rates while ensuring fairness. A piecewise linear reward function is designed to improve convergence. Comparative results against benchmark policies demonstrate DQL’s potential to manage highly stochastic logistics operations. Key findings include: (1) Drone fleet resupply enhances regional fairness and overall service rates, demonstrating strong robustness in serving long-distance and time-sensitive deliveries; (2) An appropriate reward function significantly facilitates fairness, while penalties further improve it but are ineffective alone; (3) An optimal drone-truck fleet mix exists to balance service rates and associated costs, while adding more trucks alone substantially improves service rates but incurs larger costs; (4) The proposed approach remains effective across drone capacities, while extended drone endurance yields limited gains in service rate but significantly lowers delivery costs.