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

Christian Artigues (1) , Emmanuel Hébrard (1) , Yannick Pencolé (2) , Andreas Schutt (3) , Peter J. Stuckey (4)

1 LAAS-ROC - Équipe Recherche Opérationnelle, Optimisation Combinatoire et Contraintes
2 LAAS-DISCO - Équipe DIagnostic, Supervision et COnduite
3 CSIRO - Data61 [Canberra]
4 Department of Computing and Information Systems

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 evac-uees 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 1 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.

Domaines: Recherche opérationnelle [math.OC]

Christian Artigues, Emmanuel Hébrard, Yannick Pencolé, Andreas Schutt, Peter J. Stuckey. Data Instance generator and optimization models for evacuation planning in the event of wildfire. GEOSAFE Workshop on Robust Solutions for Fire Fighting (RSFF 2018), Jul 2018, L'Aquila, Italy. pp.75-86. ⟨hal-01814063⟩