Quantum-Enhanced Optimization of AV-Drone Fleets for Urban Last-Mile Logistics
DOI:
https://doi.org/10.4108/eetiot.11026Keywords:
Autonomous vehicles, Drone delivery, Quantum optimization, Reinforcement learning, Urban logistics, XAIAbstract
The rise of e-commerce and calls for sustainability make things hard for businesses in cities, showing where standard shipping methods fail. The study shows a new hybrid quantum-classical optimization scheme that bridges three major gaps: AV-drone coordination that does not work well in the real world; environmental claims that have not been proven; and real-world limits that are not considered enough. In a modern design, the Quantum Approximate Optimization Algorithm (QAOA) and Deep Q-Networks (DQN) are put together. There are two main variables used for optimization: regulatory factors (β =0.32) and energy limits (β =0.56). The framework cuts costs by 28% and speeds up delivery times by 32%, while keeping 89% of solution accuracy on noisy quantum simulations. Run 1,000 Monte Carlo models and compare them to real data to do this. Checking the methods with three steps—computer simulation, stakeholder analysis (n=25, ±=0.81), and policy paper review—is what makes sure they work. The results make it possible to measure the difference in success between models and real life. They show that in the real world, savings of 40% in efficiency drop to 22%. It turns out that regulatory division is the main problem, causing 92% of practical errors. The study produces the idea of energy-constrained quantum advantage and shows that it can only be useful with solid-state batteries (≥400 Wh/kg) and regulatory unification. These efforts set new standards for urban mobility research that involves multiple fields and show how driverless operations can be used in a way that is viable.
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