Unmanned Aerial Vehicle Traffic Network Design with Risk Mitigation

Abstract

As unmanned aerial vehicle (UAV) technology becomes more robust and widespread, more and more retail companies are seeing UAVs as a suitable alternative to ground-based transportation to deliver their packages. As a result, there has been an abundance of OR research focused on UAV utilization for last-mile delivery. Due to the size and mobility of UAVs, most of this research considers UAV movement within a shortest path or Euclidean shortest path context. While this may be plausible if drone usage remains sparse, this framework will not be possible as drone utilization ramps up to the levels required to satisfy the levels of package demand expected in the coming decades. Furthermore, none of this prior research (to our knowledge) suggests using risk inherent with UAV travel to influence their proposals from a logistical and/or modelling perspective. As a solution to this problem, our industry partner AirMatrix proposes that UAV travel be restricted to transportation networks situated above the streets of population centres. We propose a bi-objective network selection model for drone delivery which minimizes risk while maximizing the amount of satisfied demand subject to budgetary constraints. We discuss the factors that affect UAV risk and what metrics can be used to effectively reduce those factors from a modelling perspective. We propose a two-stage stochastic variant of the model and additional problem requirements to reflect practical operational requirements and design goals. Using sample average approximation, we show that a deterministic solution is effectively as good as an associated stochastic solution. We conduct testing on a region of suburban Miami to evaluate how different risk objectives perform with respect to network, path, arc, and performance metrics.