Mauno Pesonen, Arto Kettunen, Petri Räsänen. (1995). Non-industrial private forest landowners’ choices of timber management strategies. Acta Forestalia Fennica no. 250 article id 7512. https://doi.org/10.14214/aff.7512

Keywords: forest owners; timber management strategies; genetic algorithm; strategic decision making

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The factors affecting the non-industrial, private forest owners’ (NIPF) strategic decisions in management planning are studied. A genetic algorithm is used to induce a set of rules predicting potential cut of the forest owners’ choices of preferred timber management strategies. The rules are based on variables describing the characteristics of the landowners and their forest holdings. The predictive ability of a genetic algorithm is compared to linear regression analysis using identical data sets. The data are cross-validated seven times applying both genetic algorithm and regression analyses in order to examine the data-sensitivity and robustness of the generated models.

The optimal rule set derived from genetic algorithm analyses included the following variables: mean initial volume, forest owner’s positive price expectations for the next eight years, forest owner being classified as farmer, and preference for the recreational use of forest property. When tested with previously unseen test data, the optimal rule set resulted in a relative root mean square error of 0.40.

In the regression analyses, the optimal regression equation consisted of the following variables: mean initial volume, proportion of forestry income, intention to cut extensively in future, and positive price expectations for the next two years. The R² of the optimal regression equation was 0.3 and the relative root mean square error from the test data 0.38.

In both models, mean initial volume and positive stumpage price expectations were entered as significant predictors of potential cut of preferred timber management strategy. When tested with complete data set of 201 observations, both the optimal rule set and the optimal regression model achieved the same level of accuracy.

Pesonen, E-mail: mp@mm.unknown
Kettunen, E-mail: ak@mm.unknown
Räsänen, E-mail: pr@mm.unknown

Category : Research article

article id 299, category Research article

Hongcheng Zeng, Timo Pukkala, Heli Peltola, Seppo Kellomäki. (2007). Application of ant colony optimization for the risk management of wind damage in forest planning. Silva Fennica vol. 41 no. 2 article id 299. https://doi.org/10.14214/sf.299

Keywords: harvest scheduling; heuristics; genetic algorithms; simulated annealing; spatial optimization

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Ant colony optimization (ACO) is still quite a new technique and seldom used in the field of forest planning compared to other heuristics such as simulated annealing and genetic algorithms. This work was aimed at evaluating the suitability of ACO for optimizing the clear-cut patterns of a forest landscape when aiming at simultaneously minimizing the risk of wind damage and maintaining sustainable and even flow of periodical harvests. For this purpose, the ACO was first revised and the algorithm was coded using the Visual Basic Application of the ArcGIS software. Thereafter, the performance of the modified ACO was demonstrated in a forest located in central Finland using a 30-year planning period. Its performance was compared to simulated annealing and a genetic algorithm. The revised ACO performed logically since the objective function value was improving and the algorithm was converging during the optimization process. The solutions maintained a quite even periodical harvesting timber while minimizing the risk of wind damage. Implementing the solution would result in smooth landscape in terms of stand height after the 30-year planning period. The algorithm is quite sensitive to the parameters controlling pheromone updating and schedule selecting. It is comparable in solution quality to simulated annealing and genetic algorithms.

Zeng, University of Joensuu, Faculty of Forest Sciences, P. O. Box 111, FI-80101 Joensuu, Finland E-mail: hongcheng.zeng@joensuu.fi
Pukkala, University of Joensuu, Faculty of Forest Sciences, P. O. Box 111, FI-80101 Joensuu, Finland E-mail: tp@nn.fi
Peltola, University of Joensuu, Faculty of Forest Sciences, P. O. Box 111, FI-80101 Joensuu, Finland E-mail: hp@nn.fi
Kellomäki, University of Joensuu, Faculty of Forest Sciences, P. O. Box 111, FI-80101 Joensuu, Finland E-mail: sk@nn.fi

article id 396, category Research article

Timo Pukkala, Mikko Kurttila. (2005). Examining the performance of six heuristic optimisation techniques in different forest planning problems. Silva Fennica vol. 39 no. 1 article id 396. https://doi.org/10.14214/sf.396

Keywords: genetic algorithms; simulated annealing; ecological planning; habitat suitability index (HSI); Hero; random search; tabu search

Abstract | View details | Full text in PDF | Author Info

The existence of multiple decision-makers and goals, spatial and non-linear forest management objectives and the combinatorial nature of forest planning problems are reasons that support the use of heuristic optimisation algorithms in forest planning instead of the more traditional LP methods. A heuristic is a search algorithm that does not necessarily find the global optimum but it can produce relatively good solutions within reasonable time. The performance of different heuristics may vary depending on the complexity of the planning problem. This study tested six heuristic optimisation techniques in five different, increasingly difficult planning problems. The heuristics were evaluated with respect to the objective function value that the techniques were able to find, and the time they consumed in the optimisation process. The tested optimisation techniques were 1) random ascent (RA), 2) Hero sequential ascent technique (Hero), 3) simulated annealing (SA), 4) a hybrid of SA and Hero (SA+Hero), 5) tabu search (TS) and 6) genetic algorithm (GA). The results, calculated as averages of 100 repeated optimisations, were very similar for all heuristics with respect to the objective function value but the time consumption of the heuristics varied considerably. During the time the slowest techniques (SA or GA) required for convergence, the optimisation could have been repeated about 200 times with the fastest technique (Hero). The SA+Hero and SA techniques found the best solutions for non-spatial planning problems, while GA was the best in the most difficult problems. The results suggest that, especially in spatial planning problems, it is a benefit if the method performs more complicated moves than selecting one of the neighbouring solutions. It may also be beneficial to combine two or more heuristic techniques.

Pukkala, University of Joensuu, Faculty of Forestry, P.O. BOX 111, FI-80101 Joensuu, Finland E-mail: timo.pukkala@forest.joensuu.fi
Kurttila, Finnish Forest Research Institute, Joensuu Research Centre, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: mk@nn.fi

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