Solving the optimal location problem in forest fire control with fuzzy data points

In this paper, we present a methodology to solve location problems when the data used is inherently fuzzy. This method, from data clusterized with the fuzzy c��means algorithm, calculates bi-dimensional fuzzy numbers from the clusters which are used to calculate a fuzzy solution. We apply the methodology, with different objective functions, to a particularly apt data set of forest fire breakouts in the Bouches du Rhone region of southern France, gathered from 1981 to 2009. The robustness of the method is then evaluated with a Monte Carlo simulation in which the number of clusters change. The solution provided with this fuzzy method provides leeway to planners, which can see how the membership function of the fuzzy solution can be used as a measurement of “appropriateness” of the final location.