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Applying ant colony optimization metaheuristic to solve forest transportation planning problems with side constraints

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Abstract:

Forest transportation planning problems (FTPP) have evolved from considering only the financial aspects of timber management to more holistic problems that also consider the environmental impacts of roads. These additional requirements have introduced side constraints, making FTPP larger and more complex. Mixed-integer programming (MIP) has been used to solve FTPP, but its application has been limited by the difficulty of solving large, real-world problems within a reasonable time. To overcome this limitation of MIP, we applied the ant colony optimization (ACO) metaheuristic to develop an ACO-based heuristic algorithm that efficiently solves large and complex forest transportation problems with side constraints. Three hypothetical FTPP were created to test the performance of the ACO algorithm. The environmental impact of forest roads represented by sediment yields was incorporated into the economic analysis of roads as a side constraint. Four different levels of sediment constraints were analyzed for each problem. The solutions from the ACO algorithm were compared with those obtained from a commercially available MIP solver. The ACO solutions were equal to or slightly worse than the MIP solution, but the ACO algorithm took only a fraction of the computation time that was required by the MIP solver.

Les problèmes de planification du transport forestier (PPTF) ont évolué de la prise en considération uniquement des aspects financiers de la gestion forestière vers une approche plus globale qui tient compte aussi de l’effet des routes sur l’environnement. Ces exigences additionnelles ont amené des contraintes supplémentaires qui rendent les PPTF plus volumineux et plus complexes. La programmation linéaire mixte (PLM) a été utilisée pour résoudre les PPTF, mais ses applications ont été limitées par la difficulté à résoudre des problèmes du monde réel de grandes tailles, à l’intérieur d’un délai raisonnable. Pour surmonter cette faiblesse de la PLM, nous avons appliqué la métaheuristique de la colonie de fourmis pour développer un algorithme qui résout de façon efficace les PPTF complexes et de grandes tailles avec des contraintes complémentaires. Trois PPTF hypothétiques ont été créés pour tester la performance de l’algorithme par colonie de fourmis (ACF). L’impact environnemental des routes forestières, exprimé par la production de sédiments, a été incorporé dans l’analyse économique des routes comme une contrainte complémentaire. Quatre niveaux différents des contraintes de sédimentation ont été analysés pour chaque problème. Les solutions obtenues avec l’ACF ont été comparées à celles obtenues avec un résolveur PLM commercial. Les solutions de l’ACF étaient équivalentes ou légèrement pires que la solution de la PLM, mais elles ne nécessitaient qu’une fraction du temps de calcul requis par le résolveur PLM.

Document Type: Research Article

Publication date: November 1, 2008

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  • Published since 1971, this monthly journal features articles, reviews, notes and commentaries on all aspects of forest science, including biometrics and mensuration, conservation, disturbance, ecology, economics, entomology, fire, genetics, management, operations, pathology, physiology, policy, remote sensing, social science, soil, silviculture, wildlife and wood science, contributed by internationally respected scientists. It also publishes special issues dedicated to a topic of current interest.
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