Low-Noise Route and Operations Planning for Food Delivery Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) are emerging as a promising mode of food delivery services; however, their impact on noise in urban communities has raised growing concerns. Existing research focusing on low-noise strategic route planning has yet to address the optimization of tactical operations planning, such as generating UAV dispatch schedules that consider operational constraints. On the other hand, studies on tactical operations planning have largely overlooked the noise impact of UAV routes. To address this gap, we propose an integrated optimization model that combines low-noise route planning and tactical operations planning. Our proposed model formulates a multiobjective mixed-integer linear programming problem to generate optimal UAV routes that minimize both noise disturbance and travel distances between food pickup and drop-off nodes. It also considers UAV energy limitations and determines when and where battery replacement is needed. Since food delivery is time-sensitive, involving perishable items that must reach customers while still fresh and at the right temperature, our model ensures that each customer order is delivered within its designated time window. A rolling horizon approach is used for dynamic dispatching, which iteratively updates customer orders and noise levels. We find several key operational insights that can help logistics planners optimize the size, routing, scheduling, and battery management of the UAV fleet. Our model provides a comprehensive approach that balances operational efficiency and noise reduction, helping to overcome a critical barrier to the wider public acceptance of UAV operations.