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Computational modeling demonstrates a 40% reduction in urban delivery travel times using coordinated truck-robot-drone routing, highlighting potential logistics efficiency and emission cuts.
The continued growth of online shopping in recent years has placed greater demands on efficient last-mile delivery systems. Traditional truck parcel delivery faces challenges such as slow delivery in congested urban areas and high environmental impacts. These issues have inspired researchers and practitioners to develop novel multi-modal delivery systems based on the coordination between traditional delivery trucks and newer technologies such as delivery robots and drones. In this paper, we model a synchronous truck-robot-drone last-mile delivery system and propose a mixed integer program along with a heuristic solution. The proposed model is designed for a delivery truck equipped with a multi-visit drone and multiple single-delivery robots. The model accounts for multiple operational constraints, including drone energy endurance and the load capacities of both the drone and the robots. Computational experiments are conducted to demonstrate the efficiency of the proposed heuristic in solving real-sized instances. The impact of the proposed delivery system is evaluated using the 2021 Amazon Last Mile Routing Research Challenge dataset in Seattle. The case results show that replacing the truck with the proposed system can reduce travel times within customer zones by an average of 40% compared to the original routes. Time savings can reach up to 52% for larger deliveries, demonstrating the system’s potential to improve efficiency and contribute to reduced environmental impacts in urban logistics.
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Bootaki et al. (2026) studied this question.
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