pyO3F—A Python Framework for Wildfire-Related Optimization, Part I: Design and Fundamentals

Filipe Alvelos ()
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Filipe Alvelos: School of Engineering, University of Minho, Department of Production and Systems/ALGORITMI Research Center/LASI

A chapter in Advances in Optimization and Wildfire, 2026, pp 43-57 from Springer

Abstract: Abstract The purpose of the framework pyO3F is to support the development of optimization approaches, such as meta-heuristics or mixed integer programming, for wildfire-related optimization problems. Given an extent, it allows the definition of scenarios that a user may use as a base to his approaches and/or directly to estimate fire potential and/or simulation of fire spread. The main contribution is the consideration of fire-related resources and their interaction with fire. pyO3F is based on the minimum travel time principle that states that fire follows the quickest path between any two locations in a landscape. This principle makes it possible to model fire spread as a set of shortest paths in a network. The actual or potential positions and movements of the firefighting resources are defined in networks that interact with the fire network according to their type (for example, suppression or detection). This is the first paper of two on pyO3F. In this paper, we describe the architecture of pyO3F and how fire, resources, and their interaction are modeled and implemented. In the second paper, pyO3F—A python framework for wildfire-related optimization, Part II: Usage, we describe how it can be used to provide fire scenarios, to estimate fire spread and fire potential, with or without the presence of resources. pyO3F is one of the results of the project O3F - An Optimization Framework to Reduce Forest Fires.

Keywords: Software; Ffire spread simulation; Wildfire supression; Mixed integer programming (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-03108-2_3

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