A robust p-Center problem under pressure to locate shelters in wildfire context

Marc Demange (), Virginie Gabrel (), Marcel A. Haddad () and Cécile Murat ()
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Marc Demange: School of Science RMIT University
Virginie Gabrel: Université Paris-Dauphine, PSL Research University, CNRS, LAMSADE
Marcel A. Haddad: Université Paris-Dauphine, PSL Research University, CNRS, LAMSADE
Cécile Murat: Université Paris-Dauphine, PSL Research University, CNRS, LAMSADE

EURO Journal on Computational Optimization, 2020, vol. 8, issue 2, No 1, 103-139

Abstract: Abstract The location of shelters in different areas threatened by wildfires is one of the possible ways to reduce fatalities in a context of an increasing number of catastrophic and severe wildfires. These shelters will enable the population in the area to be protected in case of fire outbreaks. The subject of our study is to determine the best place for shelters in a given territory. The territory, divided into zones, is represented by a graph in which each zone corresponds to a node and two nodes are linked by an edge if it is feasible to go directly from one zone to the other. The problem is to locate p shelters on nodes so that the maximum distance of any node to its nearest shelter is minimized. When the uncertainty of fire outbreaks is not considered, this problem corresponds to the well-known p-Center problem on a graph. In this article, the uncertainty of fire outbreaks is introduced taking into account a finite set of fire scenarios. A scenario defines a fire outbreak on a single zone with the main consequence of modifying evacuation paths. Several evacuation paths may become impracticable and the ensuing evacuation decisions made under pressure may no longer be rational. In this context, the new issue under consideration is to place p shelters on a graph so that the maximum evacuation distance of any node to its nearest shelter in any scenario is minimized. We refer to this problem as the Robust p-Center problem under Pressure. After proving the NP-hardness of this problem on subgraphs of grids, we propose a first formulation based on 0-1 Linear Programming. For real size instances, the sizes of the 0-1 Linear Programs are huge and we propose a decomposition scheme to solve them exactly. Experimental results outline the efficiency of our approach.

Keywords: Robust combinatorial optimization; Wildfire emergency; Shelter location; Integer linear programming; 68Q25; 90C17; 90D80 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s13675-020-00124-x

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