Endogenous stochastic optimisation for relief distribution assisted with Unmanned Aerial Vehicles

Preprint

Abstract

Unmanned Aerial Vehicles (UAVs) are being increasingly integrated into humanitarian operations given the growing economic pressure on organisations providing disaster relief. Among other applications, UAV-based damage assessment during relief delivery has been the focus of respondents, yet there is a lack of research into formalising a problem that considers both aspects simultaneously. This paper presents a novel endogenous stochastic vehicle routing problem that coordinates UAV deployments and relief vehicle dispatches to minimise overall mission cost. The algorithm considers uncertain damage levels in a transport network, with UAVs revealing actual damage levels by performing rapid network assessment. Ground vehicles are simultaneously routed based on the information gathered by the UAVs. A greedy extact solution approach and an adapted Genetic Algorithm are used to solve a case study based on the 2010 Haiti earthquake. Both approaches provide significant improvements in vehicle travel time over a deterministic approach reported, and are used to quantify the benefits of UAV-assisted response.

Avatar
Jose Escribano-Macias
PhD Student

Researcher on humanitarian logistics, focusing on the use of drones for relief delivery

Avatar
Nils Goldbeck
PhD Student

Final-year PhD student, focusing on resilience of interdependent critical infrastructure systems.

Avatar
Leo Hsu
Post-Doctoral Researcher

Recently obtained PhD from the Dyson School of Design Engineering. Currently focusing on the Demand Responsive Transport Systems.

Avatar
Panagiotis Angeloudis
Associate Professor

Associate Professor in Transport Systems and Logistics, with a passion for CS, OR and their role in transforming transportation.