Data instance generator and optimization models for evacuation planning in the event of wildfire

Christian Artigues, Emmanuel Hébrard, Yannick Pencolé, Andreas Schutt, Peter J. Stuckey

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

One critical part of decision support during the response phase to a wildfire is the ability to perform large-scale evacuation planning. While in practice most evacuation planning is principally designed by experts using simple heuristic approaches or scenario simulations, more recently optimization approaches to evacuation planning have been carried out, notably in the context of floodings. Evacuation planning in case of wildfires is much harder as wildfire propagations are inherently less predictable than floods. This paper present a new optimization model for evacuation planning in the event of wildfire aiming at maximizing the temporal safety margin between the evacuees and the actual or potential wildfire front. As a first contribution, an open-source data instance generator based on road network generation via quadtrees and a basic fire propagation model is proposed to the community. As a second contribution we propose 0-1 integer programming and constraint programming formulations enhanced with a simple compression heuristic that are compared on 240 problem instances build by the generator. The results show that the generated instances are computationally challenging and that the contraint programming framework obtains the best performance.

Original languageEnglish
Title of host publicationRSFF 2018, Robust Solutions for Fire Fighting
Subtitle of host publicationProceedings of the GEOSAFE Workshop on Robust Solutions for Fire Fighting, L'Aquila, Italy, July 19-20, 2018
EditorsGabriele Di Stefano, Alfredo Navarra
Place of PublicationItaly
PublisherRheinisch-Westfaelische Technische Hochschule Aachen
Pages75-86
Number of pages12
Volume2146
ISBN (Electronic)007421461
Publication statusPublished - 2018
Externally publishedYes
EventGEOSAFE Workshop on Robust Solutions for Fire Fighting 2018 - L'Aquila, Italy
Duration: 19 Jul 201820 Jul 2018
http://ceur-ws.org/

Publication series

NameCEUR Workshop Proceedings
PublisherRheinisch-Westfaelische Technische Hochschule Aachen * Lehrstuhl Informatik V
ISSN (Print)1613-0073

Conference

ConferenceGEOSAFE Workshop on Robust Solutions for Fire Fighting 2018
Abbreviated titleRSFF 2018
CountryItaly
CityL'Aquila
Period19/07/1820/07/18
Internet address

Cite this

Artigues, C., Hébrard, E., Pencolé, Y., Schutt, A., & Stuckey, P. J. (2018). Data instance generator and optimization models for evacuation planning in the event of wildfire. In G. Di Stefano, & A. Navarra (Eds.), RSFF 2018, Robust Solutions for Fire Fighting: Proceedings of the GEOSAFE Workshop on Robust Solutions for Fire Fighting, L'Aquila, Italy, July 19-20, 2018 (Vol. 2146, pp. 75-86). (CEUR Workshop Proceedings). Rheinisch-Westfaelische Technische Hochschule Aachen. http://ceur-ws.org/Vol-2146/paperA4.pdf