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Endogenous stochastic optimisation for relief distribution assisted with Unmanned Aerial Vehicles

Jose Javier Escribano Macias(jose.escribano-macias11***at***imperial.ac.uk)
Nils Goldbeck(n.goldbeck14***at***imperial.ac.uk)
Pei-Yuan Hsu(p.hsu15***at***imperial.ac.uk)
Panagiotis Angeloudis(p.angeloudis***at***imperial.ac.uk)
Washington Ochieng(w.ochieng***at***imperial.ac.uk)

Abstract: Unmanned Aerial Vehicles (UAVs) are being increasingly integrated into humanitarian operations given the growing economic pressure on organisa- tions 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 simul- taneously. 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 lev- els in a transport network, with UAVs used to survey actual damage levels by performing rapid network assessment. Ground vehicles are simultaneously routed based on the information gathered by the UAVs. A greedy exact so- lution approach and an adapted Genetic Algorithm are used to solve a case study on a road network in Haiti. Both approaches provide signi cant im- provements in vehicle travel time compared to a basic deterministic approach, and are used to quantify the bene ts of UAV-assisted response.

Keywords: Relief optimisation; Endogenous uncertainty; Damage Assessment; Unmanned Aerial Vehicles

Category 1: Applications -- OR and Management Sciences (Transportation )

Citation: Imperial College London, 12/2018

Download: [PDF]

Entry Submitted: 12/01/2018
Entry Accepted: 12/01/2018
Entry Last Modified: 12/01/2018

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