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Articles containing the keyword 'adjacency constraints'.

Category: Research article

article id 1232, category Research article
Pete Bettinger, Mehmet Demirci, Kevin Boston. (2015). Search reversion within s-metaheuristics: impacts illustrated with a forest planning problem. Silva Fennica vol. 49 no. 2 article id 1232. https://doi.org/10.14214/sf.1232
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.
The 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.
  • Bettinger, School of Forestry and Natural Resources, 180 E. Green Street, University of Georgia, Athens, Georgia, USA 30602 ORCID ID:E-mail: pbettinger@warnell.uga.edu (email)
  • Demirci, General Directorate of Forestry, Ministry of Forest and Water Affairs, Republic of Turkey ORCID ID:E-mail: mehmetdemirci@yahoo.com
  • Boston, Department of Forest Engineering, Resources and Management, College of Forestry, Oregon State University, USA ORCID ID:E-mail: Kevin.Boston@oregonstate.edu
article id 545, category Research article
Pete Bettinger, David Graetz, Kevin Boston, John Sessions, Woodam Chung. (2002). Eight heuristic planning techniques applied to three increasingly difficult wildlife planning problems. Silva Fennica vol. 36 no. 2 article id 545. https://doi.org/10.14214/sf.545
As both spatial and temporal characteristics of desired future conditions are becoming important measures of forest plan success, forest plans and forest planning goals are becoming complex. Heuristic techniques are becoming popular for developing alternative forest plans that include spatial constraints. Eight types of heuristic planning techniques were applied to three increasingly difficult forest planning problems where the objective function sought to maximize the amount of land in certain types of wildlife habitat. The goal of this research was to understand the relative challenges and opportunities each technique presents when more complex difficult goals are desired. The eight heuristic techniques were random search, simulated annealing, great deluge, threshold accepting, tabu search with 1-opt moves, tabu search with 1-opt and 2-opt moves, genetic algorithm, and a hybrid tabu search / genetic algorithm search process. While our results should not be viewed as universal truths, we determined that for the problems we examined, there were three classes of techniques: very good (simulated annealing, threshold accepting, great deluge, tabu search with 1-opt and 2-opt moves, and tabu search / genetic algorithm), adequate (tabu search with 1-opt moves, genetic algorithm), and less than adequate (random search). The relative advantages in terms of solution time and complexity of programming code are discussed and should provide planners and researchers a guide to help match the appropriate technique to their planning problem. The hypothetical landscape model used to evaluate the techniques can also be used by others to further compare their techniques to the ones described here.
  • Bettinger, Department of Forest Resources, Oregon State University, Corvallis, OR 97331 ORCID ID:E-mail: pete.bettinger@orst.edu (email)
  • Graetz, Department of Forest Resources, Oregon State University, Corvallis, OR 97331 ORCID ID:E-mail:
  • Boston, Carter Holt Harvey Forest Fibre Solutions, Tokoroa, New Zealand ORCID ID:E-mail:
  • Sessions, Department of Forest Engineering, Oregon State University, Corvallis, OR 97331 ORCID ID:E-mail:
  • Chung, Department of Forest Engineering, Oregon State University, Corvallis, OR 97331 ORCID ID:E-mail:
article id 605, category Research article
Paul C. Van Deusen. (2001). Scheduling spatial arrangement and harvest simultaneously. Silva Fennica vol. 35 no. 1 article id 605. https://doi.org/10.14214/sf.605
A method based on the Metropolis algorithm is developed for creating desirable spatial configurations on the landscape while simultaneously dealing with other objectives commonly associated with harvest scheduling. Spatial configurations are loosely specified and stochastically attained, which contrasts with other adjacency constraints based on specific block size limits. This method makes it possible to improve habitat and connectivity, and to create buffer zones as part of the scheduling process. It also works with a mapped set of polygons/forest stands and does not require a gridded system.
  • Van Deusen, NCASIS, Northeast Regional Center, 600 Suffolk Street, Fifth Floor, Lowell, MA 01854, USA ORCID ID:E-mail: pvandeusen@ncasi.org (email)

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