A case study of forest management planning using a new heuristic algorithm

Seo, Jakyeom; Vilčko, František; Orois, Sofía Sánchez; Kunth, Stephan; Son, Yeong Mo; Gadow, Klaus von

doi:10.1093/treephys/25.7.929

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…However, a higher number of possible combinations may also considerably increase the processing time (Hinrichs, 2006;Seo et al, 2005). We found that about 21 options per stand represents a reasonable compromise and that processing times even for large forests with up to 1500 compartments are satisfactory using an ordinary personal computer.…”

Section: Discussionmentioning

confidence: 79%

Combining spatial and other objectives in forest design

Chen

Gadow

2008

Forestry Studies / Metsanduslikud Uurimused

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Abstract. The paper presents a forest planning model for Northern Germany with an objective function which includes an economic, even flow and spatial component. The spatial modeling approach followed in this analysis relies on specific geographic relations among neighboring stands, including the distance between stand centroids and the length of the common boundary between adjacent compartments. To our knowledge, this is a new and more realistic approach in spatial optimization when compared with earlier approaches. The geographic data are derived from a GIS system and stored in a database. The database also includes details of all management regimes and the corresponding objective function coefficients. The method is first applied in a relatively small forest with only 41 compartments. The Simulated Annealing algorithm is used to identify the optimum combination of management regimes. Solutions are presented for different objective function components. When the even flow and spatial components were added in the objective function, the economic objective was only moderately reduced. The method is then also applied using a bigger example, a forest with 591 compartments. The results, obtained within a reasonable processing time, were similar and, because of the large number of compartments, the even flow was perfectly balanced.

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Section: Discussionmentioning

confidence: 79%

Combining spatial and other objectives in forest design

Chen

Gadow

2008

Forestry Studies / Metsanduslikud Uurimused

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“…Many other applications of metaheuristics to forest management issues have been explored. As examples, Seo et al (2005) presented an approach to locate near-optimal silvicultural development paths for Norway spruce (Picea abies) stands using simulated annealing. Bettinger et al (2007) developed a tabu search model to select management actions for individual forest stands to both improve forest health and to meet a higher-level landscape objectives.…”

Section: Introductionmentioning

confidence: 99%

Search reversion within s-metaheuristics: impacts illustrated with a forest planning problem

Bettinger¹,

Demirci²,

Boston³

2015

Silva Fenn.

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Search reversion within s-metaheuristics: impacts illustrated with a forest planning problemBettinger P., Demirci M., Boston K. (2015). Search reversion within s-metaheuristics: impacts illustrated with a forest planning problem. Silva Fennica vol. 49 no. 2 article id 1232. 20 p. Highlights• The interruption of the sequence of events used to explore a solution space and develop a forest plan, and the re-initiation of the search process from a high-quality, known starting point (reversion) seems necessary for some s-metaheuristics.• When using a s-metaheuristic, higher quality forest plans may be developed when the reversion interval is around six iterations of the model. AbstractThe use of a reversion technique during the search process of s-metaheuristics has received little attention with respect to forest management and planning problems. Reversion involves the interruption of the sequence of events that are used to explore the solution space and the re-initiation of the search process from a high-quality, known starting point. We explored four reversion rates when applied to three different types of s-metaheuristics that have previously shown promise for the forest planning problem explored, threshold accepting, tabu search, and the raindrop method. For two of the s-metaheuristics, we also explored three types of decision choices, a change to the harvest timing of a single management unit (1-opt move), the swapping of two management unit's harvest timing (2-opt moves), and the swapping of three management unit's harvest timing (3-opt moves). One hundred independent forest plans were developed for each of the metaheuristic / reversion rate combinations, all beginning with randomly-generated feasible starting solutions. We found that (a) reversion does improve the quality of the solutions generated, and (b) the rate of reversion is an important factor that can affect solution quality.

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