Locating and Platforms and Scheduling a Fleet of Drones for Emergency Delivery of Perishable Items

TL;DR

This paper develops models for locating drone platforms and scheduling deliveries for emergency perishables, considering perishability, time preferences, and platform mobility, with practical solutions and computational insights.

Contribution

It introduces a novel optimization framework for drone platform placement and delivery scheduling, including a two-period model with platform relocation, addressing NP-hard problems with practical discretized solutions.

Findings

Discretized time-slot models are solvable with standard optimization tools.

Platform mobility significantly impacts delivery efficiency.

Computational experiments reveal trade-offs between drone range, fleet size, and delivery timeliness.

Abstract

Motivated by issues dealing with delivery of emergency medical products during humanitarian disasters, this paper addresses the general problem of delivering perishable items to remote demands accessible only by helicopters or drones. Each drone operates out of platforms that may be move when not in use and each drone has a limited delivery range to service a demand points. Associated with each demand point is a disutility function, or a cost function, with respect to time that reflects preferred delivery clock time for the demanded item, as well as the item's perishability characteristic that models nonincreasing quality with time. The paper first addresses the problem of locating the platforms as well concurrently determining which platform serves which demand points and in what order to minimize total disutility for product delivery. The second scenario addresses the two-period problem where the platforms can be relocated, using useable road networks, after the first period. It can be easily proven that continuous time versions of these problems are NP-Hard. However, a practical "time-slot" version of the problem, where time is discretized into slots, can be solved by standard optimization software. Extensive computational experiments, using different drone delivery ranges as well as different drone fleet sizes, provide valuable insights on the performance of such drone delivery systems.