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Articles containing the keyword 'heuristics'.

Category: Research article

article id 1622, category Research article
Lingbo Dong, Pete Bettinger, Zhaogang Liu, Huiyan Qin, Yinghui Zhao. (2016). Evaluating the neighborhood, hybrid and reversion search techniques of a simulated annealing algorithm in solving forest spatial harvest scheduling problems. Silva Fennica vol. 50 no. 4 article id 1622. https://doi.org/10.14214/sf.1622
Highlights: The performances of neighborhood, hybrid and reversion search strategies of simulated annealing were evaluated when implemented with a forest spatial harvest scheduling problem; The performances of alternative search strategies of simulated annealing were all systematic and clear better than the conventional strategy; The reversion techniques were significant superior to the other search strategies in solving forest spatial harvest scheduling problems.

Heuristic techniques have been increasingly used to address the complex forest planning problems over the last few decades. However, heuristics only can provide acceptable solutions to difficult problems, rather than guarantee that the optimal solution will be located. The strategies of neighborhood, hybrid and reversion search processes have been proved to be effective in improving the quality of heuristic results, as suggested recently in the literature. The overall aims of this paper were therefore to systematically evaluate the performances of these enhanced techniques when implemented with a simulated annealing algorithm. Five enhanced techniques were developed using different strategies for generating candidate solutions. These were then compared to the conventional search strategy that employed 1-opt moves (Strategy 1) alone. The five search strategies are classified into three categories: i) neighborhood search techniques that only used the change version of 2-opt moves (Strategy 2); ii) self-hybrid search techniques that oscillate between 1-opt moves and the change version of 2-opt moves (Strategy 3), or the exchange version of 2-opt moves (Strategy 4); iii) reversion search techniques that utilize 1-opt moves and the change version of 2-opt moves (Strategy 5) or the exchange version of 2-opt moves (Strategy 6). We found that the performances of all the enhanced search techniques of simulated annealing were systematic and often clear better than conventional search strategy, however the required computational time was significantly increased. For a minimization planning problem, Strategy 6 produced the lowest mean objective function values, which were less than 1% of the means developed using conventional search strategy. Although Strategy 6 performed very well, the other search strategies should not be neglected because they also have the potential to produce high-quality solutions.

  • Dong, Department of Forest Management, 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
  • Liu, Department of Forest Management, College of Forestry, Northeast Forestry University, Harbin 150040, China ORCID ID:E-mail: lzg19700602@163.com (email)
  • Qin, Department of Forestry Economic, College of Economic & Management, Northeast Forestry University, Harbin 150040, China ORCID ID:E-mail: huiyanqin@hotmail.com
  • Zhao, Department of Forest Management, College of Forestry, Northeast Forestry University, Harbin 150040, China ORCID ID:E-mail: zyinghui0925@126.com
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 276, category Research article
Jianping Zhu, Pete Bettinger, Rongxia Li. (2007). Additional insight into the performance of a new heuristic for solving spatially constrained forest planning problems. Silva Fennica vol. 41 no. 4 article id 276. https://doi.org/10.14214/sf.276
The raindrop method of searching a solution space for feasible and efficient forest management plans has been demonstrated as being useful under a limited set of circumstances, mainly where adjacency restrictions are accommodated using the unit restriction model. We expanded on this work and applied the model (in a modified form) to a problem that had both wood flow and area restriction adjacency constraints, then tested the problem formulation on six hypothetical forests of different sizes and age class distributions. Threshold accepting and tabu search were both applied to the problems as well. The modified raindrop method’s performance was best when applied to forests with normal age class distributions. 1-opt tabu search worked best on forests with young age class distributions. Threshold accepting and the raindrop method both performed well on forests with older age class distributions. On average, the raindrop method produced higher quality solutions for most of the problems, and in all but one case where it did not, the solutions generated were not significantly different than the heuristic that located a better solution. The advantage of the raindrop method is that it uses only two parameters and does not require extensive parameterization. The disadvantage is the amount of time it needs to solve problems with area restriction adjacency constraints. We suggest that it may be advantageous to use this heuristic on problems with relatively simple spatial forest planning constraints, and problems that do not involve young initial age class distributions. However, generalization of the performance of the raindrop method to other forest planning problems is problematic, and will require examination by those interested in pursuing this planning methodology. Given that our tests of the raindrop method are limited to a small set of URM and ARM formulations, one should view the combined set of work as additional insight into the potential performance of the method on problems of current interest to the forest planning community.
  • Zhu, Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA ORCID ID:E-mail:
  • Bettinger, Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA ORCID ID:E-mail: pbettinger@warnell.uga.edu (email)
  • Li, Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA ORCID ID:E-mail:
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
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 ORCID ID:E-mail: hongcheng.zeng@joensuu.fi (email)
  • Pukkala, University of Joensuu, Faculty of Forest Sciences, P. O. Box 111, FI-80101 Joensuu, Finland ORCID ID:E-mail:
  • Peltola, University of Joensuu, Faculty of Forest Sciences, P. O. Box 111, FI-80101 Joensuu, Finland ORCID ID:E-mail:
  • Kellomäki, University of Joensuu, Faculty of Forest Sciences, P. O. Box 111, FI-80101 Joensuu, Finland ORCID ID:E-mail:
article id 578, category Research article
Kevin Boston, Pete Bettinger. (2001). Development of spatially feasible forest plans: a comparison of two modeling approaches. Silva Fennica vol. 35 no. 4 article id 578. https://doi.org/10.14214/sf.578
Spatial goals are becoming more frequent aspects of forest management plans as regulatory and organizational policies change in response to fisheries and wildlife concerns. The combination of green-up constraints (harvesting restrictions that prevent the cutting of adjacent units for a specified period of time) and habitat requirements for red-cockaded woodpeckers (RCW) in the southeastern U.S. suggests that spatially feasible forest plans be developed to guide management activities. We examined two modeling approaches aimed at developing management plans that had both harvest volume goals, RCW habitat, and green-up constraints. The first was a two-stage method that in one stage used linear programming to assign volume goals, and in a second stage used a tabu search – genetic algorithm heuristic technique to minimize the deviations from the volume goals while maximizing the present net revenue and addressing the RCW and green-up constraints. The second approach was a one-stage procedure where the entire management plan was developed with the tabu search – genetic algorithm heuristic technique, thus it did not use the guidance for timber volume levels provided by the LP solution. The goal was to test two modeling approaches to solving a realistic spatial harvest scheduling problem. One is where to volume goals are calculated prior to developing the spatially feasible forest plan, while the other approach simultaneously addresses the volume goals while developing the spatially feasible forest plan. The resulting forest plan from the two-stage approach was superior to that produced from the one-stage approach in terms of net present value. The main point from this analysis is that heuristic techniques may benefit from guidance provided by relaxed LP solutions in their effort to develop efficient forest management plans, particularly when both commodity production and complex spatial wildlife habitat goals are considered. Differences in the production of forest products were apparent between the two modeling approaches, which could have a significant effect on the selection of wood processing equipment and facilities.
  • Boston, Forest Fibre Solutions, Carter Holt Harvey, Tokoroa, New Zealand ORCID ID:E-mail: kevin.boston@chh.co.nz (email)
  • Bettinger, Department of Forest Resources, Oregon State University, Corvallis, OR 97331 ORCID ID:E-mail:

Category: Article

article id 5636, category Article
Timo Pukkala, Jyrki Kangas, Matleena Kniivilä, Anne-Mari Tiainen. (1997). Integrating forest-level and compartment-level indices of species diversity with numerical forest planning. Silva Fennica vol. 31 no. 4 article id 5636. https://doi.org/10.14214/sf.a8538

The study proposes a technique which enables the computation of user-defined indices for species diversity. These indices are derived from characteristics, called diversity indicators, of inventory plots, stand compartments, and the whole forest holding. The study discusses the modifications required to be made to typical forest planning systems due to this kind of biodiversity computation. A case study illustrating the use of the indices and a modified forest planning system is provided. In the case study, forest-level species diversity index was computed from the volume of dead wood, volume of broadleaved trees, area of old forest, and between-stand variety.

At the stand level, the area of old forest was replaced by stand age, and variety was described by within-stand variety. All but one of the indicators were further partitioned into two to four sub-indicators. For example, the volume of broadleaved trees was divided into volumes of birch, aspen, willow, and other tree species. The partial contribution of an indicator to the diversity index was obtained from a sub-priority function, determined separately for each indicator. The diversity index was obtained when the partial contributions were multiplied by the weights of the corresponding indicators and then were summed. The production frontiers computed for the harvested volume and diversity indices were concave, especially for the forest-level diversity index, indicating that diversity can be maintained at satisfactory level with medium harvest levels.

  • Pukkala, ORCID ID:E-mail:
  • Kangas, ORCID ID:E-mail:
  • Kniivilä, ORCID ID:E-mail:
  • Tiainen, ORCID ID:E-mail:
article id 7513, category Article
Jyrki Kangas, Teppo Loikkanen, Timo Pukkala, Jouni Pykäläinen. (1996). A participatory approach to tactical forest planning. Acta Forestalia Fennica no. 251 article id 7513. https://doi.org/10.14214/aff.7513

The paper examines the needs, premises and criteria for effective public participation in tactical forest planning. A method for participatory forest planning utilizing the techniques of preference analysis, professional expertise and heuristic optimization is introduced. The techniques do not cover the whole process of participatory planning, but are applied as a tool constituting the numerical core for decision support. The complexity of multi-resource management is addressed by hierarchical decision analysis which assesses the public values, preferences and decision criteria toward the planning situation. An optimal management plan is sought using heuristic optimization. The plan can further be improved through mutual negotiations, if necessary. The use of the approach is demonstrated with an illustrative example. Its merits and challenges for participatory forest planning and decision making are discussed and a model for applying it in general forest planning context is depicted. By using the approach, valuable information can be obtained about public preferences and the effects of taking them into consideration on the choice of the combination of standwise treatment proposals for a forest area. Participatory forest planning calculations, carried out by the approach presented in the paper, can be utilized in conflict management and in developing compromises between competing interests.

  • Kangas, ORCID ID:E-mail:
  • Loikkanen, ORCID ID:E-mail:
  • Pukkala, ORCID ID:E-mail:
  • Pykäläinen, ORCID ID:E-mail:

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