Job Selection in a Network of Autonomous UAVs for Delivery of Goods

TL;DR

This paper compares job selection policies for autonomous UAV delivery networks, analyzing their stability, impact on delivery times, and providing a decision-making framework balancing costs and service quality.

Contribution

It introduces a methodological approach for optimizing UAV delivery systems considering stability, timing policies, and cost-service trade-offs.

Findings

Nearest job first policy can cause system instability under certain conditions.

Timing of job selection significantly affects delivery time and system stability.

A lower bound for infrastructure costs needed to meet delivery time targets is provided.

Abstract

This article analyzes two classes of job selection policies that control how a network of autonomous aerial vehicles delivers goods from depots to customers. Customer requests (jobs) occur according to a spatio-temporal stochastic process not known by the system. If job selection uses a policy in which the first job (FJ) is served first, the system may collapse to instability by removing just one vehicle. Policies that serve the nearest job (NJ) first show such threshold behavior only in some settings and can be implemented in a distributed manner. The timing of job selection has significant impact on delivery time and stability for NJ while it has no impact for FJ. Based on these findings we introduce a methodological approach for decision-making support to set up and operate such a system, taking into account the trade-off between monetary cost and service quality. In particular, we compute a lower bound for the infrastructure expenditure required to achieve a certain expected delivery time. The approach includes three time horizons: long-term decisions on the number of depots to deploy in the service area, mid-term decisions on the number of vehicles to use, and short-term decisions on the policy to operate the vehicles.