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Articles containing the keyword 'adjacency constraint'.

Category: Research article

article id 7803, category Research article
Lingbo Dong, Pete Bettinger, Huiyan Qin, Zhaogang Liu. (2018). Reflections on the number of independent solutions for forest spatial harvest scheduling problems: a case of simulated annealing. Silva Fennica vol. 52 no. 1 article id 7803. https://doi.org/10.14214/sf.7803
Highlights: No one particular neighborhood search technique of simulated annealing was found to be universally acceptable; The optimal number of independent solutions necessary for addressing the area restriction harvest scheduling model was described with a negative logarithmic function that was related with the problem size. However, optimal number of independent solutions necessary was not sensitive to the problem size for non-spatial and unit restriction harvest scheduling model problems, which should be somewhat above 250 independent runs; The types of adjacency constraints have moderate effects on the number of independent solutions, but these effects are not significant.

To assess the quality of results obtained from heuristics through statistical procedures, a number of independently generated solutions to the same problem are required, however the knowledge of how many solutions are necessary for this purpose using a specific heuristic is still not clear. Therefore, the overall aims of this paper are to quantitatively evaluate the effects of the number of independent solutions generated on the forest planning objectives and on the performance of different neighborhood search techniques of simulated annealing (SA) in three increasing difficult forest spatial harvest scheduling problems, namely non-spatial model, area restriction model (ARM) and unit restriction model (URM). The tested neighborhood search techniques included the standard version of SA using the conventional 1-opt moves, SA using the combined strategy that oscillates between the conventional 1-opt moves and the exchange version of 2-opt moves, and SA using the change version of 2-opt moves. The obtained results indicated that the number of independent solutions generated had clear effects on the conclusions of the performances of different neighborhood search techniques of SA, which indicated that no one particular neighborhood search technique of SA was universally acceptable. The optimal number of independent solutions generated for all alternative neighborhood search techniques of SA for ARM problems could be estimated using a negative logarithmic function based on the problem size, however the relationships were not sensitive (i.e., 0.13 < p < 0.78) to the problem size for non-spatial and URM harvest scheduling problems, which should be somewhat above 250 independent runs. The types of adjacency constraints did moderately affect the number of independent solutions necessary, but not significantly. Therefore, determining an optimal number of independent solutions generated is a necessary process prior to employing heuristics in forest management planning practices.

  • Dong, College of Forestry, Northeast Forestry University, Harbin 150040, China ORCID ID:E-mail: farrell0503@126.com
  • Bettinger, Warnell School of Forestry and Natural Resources, University of Georgia, Athens 30602, GA, USA ORCID ID:E-mail: pbettinger@warnell.uga.edu
  • Qin, College of Economic and Management, Northeast Forestry University, Harbin 150040, China ORCID ID:E-mail: huiyanqin@hotmail.com
  • Liu, College of Forestry, Northeast Forestry University, Harbin 150040, China ORCID ID:E-mail: lzg19700602@163.com (email)
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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