Facility Location with Congestion and Priority in Drone-Based Emergency Delivery

TL;DR

This paper develops a comprehensive facility location model for drone-based emergency delivery, optimizing placement, capacity, and demand allocation while accounting for system congestion and priority rules, providing practical operational insights.

Contribution

It introduces a novel queue-based modeling approach for drone delivery systems with multiple priority disciplines, solved via MISOCP, and offers extensive computational analysis and operational recommendations.

Findings

Model accurately predicts system congestion and response times.

Priority disciplines significantly impact delivery performance.

Operational guidelines are provided based on system parameters.

Abstract

Thanks to their fast delivery, reduced traffic restrictions, and low manpower need, drones have been increasingly deployed to deliver time-critical materials, such as medication, blood, and exam kits, in emergency situations. This paper considers a facility location model of using drones as mobile servers in emergency delivery. The model jointly optimizes the location of facilities, the capacity of drones deployed at opened facilities, and the allocation of demands, with an objective of equitable response times among all demand sites. To this end, we employ queues to model the system congestion of drone requests and consider three queuing disciplines: non-priority, static priority, and dynamic priority. For each discipline, we approximate the model as a mixed-integer second-order conic program (MISOCP), which can readily be solved in commercial solvers. We conduct extensive computational experiments to demonstrate the effectiveness and accuracy of our approach. Additionally, we compare the system performance under the three queuing disciplines and various problem parameters, from which we produce operational recommendations to decision makers in emergency delivery.