Endogenous stochastic optimisation for relief distribution assisted with unmanned aerial vehicles

Jose Escribano Macias (), Nils Goldbeck, Pei-Yuan Hsu, Panagiotis Angeloudis and Washington Ochieng
Additional contact information
Jose Escribano Macias: Imperial College London
Nils Goldbeck: Imperial College London
Pei-Yuan Hsu: Imperial College London
Panagiotis Angeloudis: Imperial College London
Washington Ochieng: Imperial College London

OR Spectrum: Quantitative Approaches in Management, 2020, vol. 42, issue 4, No 9, 1089-1125

Abstract: Abstract Unmanned aerial vehicles (UAVs) have been increasingly viewed as useful tools to assist humanitarian response in recent years. While organisations already employ UAVs for damage assessment during relief delivery, 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 and relief vehicle deployments to minimise overall mission cost. The algorithm considers stochastic damage levels in a transport network, with UAVs surveying the network to determine the actual network damages. Ground vehicles are simultaneously routed based on the information gathered by the UAVs. A case study based on the Haiti road network is solved using a greedy solution approach and an adapted genetic algorithm. Both methods provide a significant improvement in vehicle travel time compared to a deterministic approach and a non-assisted relief delivery operation, demonstrating the benefits of UAV-assisted response.

Keywords: Relief optimisation; Endogenous uncertainty; Damage assessment; Unmanned aerial vehicles (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s00291-020-00602-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:orspec:v:42:y:2020:i:4:d:10.1007_s00291-020-00602-z

Ordering information: This journal article can be ordered from
http://www.springer. ... research/journal/291

DOI: 10.1007/s00291-020-00602-z

Access Statistics for this article

OR Spectrum: Quantitative Approaches in Management is currently edited by Rainer Kolisch

More articles in OR Spectrum: Quantitative Approaches in Management from Springer, Gesellschaft für Operations Research e.V.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().