Solving the Long-Term Forest Treatment Scheduling Problem

Martin Stølevik (), Geir Hasle () and Oddvar Kloster ()
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Martin Stølevik: SINTEF ICT, Applied Mathematics
Geir Hasle: SINTEF ICT, Applied Mathematics
Oddvar Kloster: SINTEF ICT, Applied Mathematics

A chapter in Geometric Modelling, Numerical Simulation, and Optimization, 2007, pp 437-473 from Springer

Abstract: Abstract The Long-Term Forest Treatment Scheduling Problem (LTFTSP) is the task of allocating treatments in a forest such that both sustainability and economic outcome is maximized. Solving such problems is demanded in more and more countries and the task is increasingly more complex because one must adhere to local legislation, environmental issues, and public interests. To be able to handle most aspects of the LTFTSP with adjacency constraints (which is the problem we solve), a rich, spatial model which is parameterized, is required. We present a model defined on discrete land units and time points, where the treatments to perform are parameterized. Many of the most commonly used criteria in the form of constraints and objective components in long-term forestry scheduling are included. Such criteria may be defined for the complete forest region in question, or for specific sub-regions. The complexity of the model requires a robust solution method. We have selected a heuristic approach based on Tabu Search. An initial solution is constructed by composition of economically optimal schedules for each land unit. This solution is made feasible by a greedy heuristic. The initial solution is iteratively improved by Tabu Search. Two different types of move are used in the Tabu Search procedure: Shifting a treatment to another time point, and exchanging one treatment program for another treatment program. The solution method is implemented in the software tool Ecoplan. Empirical results have been produced for a 1,541 stand case from Norway. The results show that when more than one objective is included in the objective function, the quality of the solution with respect to the individual objectives may be considerably reduced. Some of the quality loss, especially with regards to the “old forest” objective component may be explained by the initial state of the forest.

Keywords: forest harvest scheduling; rich model; spatial constraints (search for similar items in EconPapers)
Date: 2007
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DOI: 10.1007/978-3-540-68783-2_13

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