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Articles containing the keyword 'harvest scheduling'.

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 1326, category Research article
Joanna Bachmatiuk, Jordi Garcia-Gonzalo, Jose Guilherme Borges. (2015). Analysis of the performance of different implementations of a heuristic method to optimize forest harvest scheduling. Silva Fennica vol. 49 no. 4 article id 1326. https://doi.org/10.14214/sf.1326
Highlights: The number of treatment schedules available for each stand has an impact on the optimal configuration of opt-moves (i.e. number stands where the treatment schedule is changed in an iteration); Considering a large number of treatment schedules per stand, the one-opt move implementation is preferred, yet when considering a low number of decision choices the two-opt moves option performs better.

Finding an optimal solution of forest management scheduling problems with even flow constraints while addressing spatial concerns is not an easy task. Solving these combinatorial problems exactly with mixed-integer programming (MIP) methods may be infeasible or else involve excessive computational costs. This has prompted the use of heuristics. In this paper we analyze the performance of different implementations of the Simulated Annealing (SA) heuristic algorithm for solving three typical harvest scheduling problems. Typically SA consists of searching a better solution by changing one decision choice in each iteration. In forest planning this means that one treatment schedule in a single stand is changed in each iteration (i.e. one-opt move). We present a comparison of the performance of the typical implementation of SA with the new implementation where up to three decision choices are changed simultaneously in each iteration (i.e. treatment schedules are changed in more than one stand). This may allow avoiding local optimal. In addition, the impact of SA - parameters (i.e. cooling schedule and initial temperature) are tested. We compare our heuristic results with a MIP formulation. The study case is tested in a real forest with 1000 stands and a total of 213116 decision choices. The study shows that when the combinatorial problem is very large, changing simultaneously the treatment schedule in more than one stand does not improve the performance of SA. Contrarily, if we reduce the size of the problem (i.e. reduce considerably the number of alternatives per stand) the two-opt moves approach performs better.

  • Bachmatiuk, Centro de Estudos Florestais, Instituto Superior de Agronomia, Universidade de Lisboa, Lisboa, Portugal ORCID ID:E-mail: jbachmatiuk@isa.ulisboa.pt (email)
  • Garcia-Gonzalo, Centro de Estudos Florestais, Instituto Superior de Agronomia, Universidade de Lisboa, Lisboa, Portugal ORCID ID:E-mail: jordigarcia@isa.ulisboa.pt
  • Borges, Centro de Estudos Florestais, Instituto Superior de Agronomia, Universidade de Lisboa, Lisboa, Portugal ORCID ID:E-mail: joseborges@isa.ulisboa.pt
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 457, category Research article
Karin Öhman, Ljusk Ola Eriksson. (2010). Aggregating harvest activities in long term forest planning by minimizing harvest area perimeters. Silva Fennica vol. 44 no. 1 article id 457. https://doi.org/10.14214/sf.457
The study presents a new approach for aggregating stands for harvest in strategic forest planning. In fragmented landscapes this could benefit nature conservation as well as reduce costs. The approach is built on the idea of minimizing the outside perimeter of contiguous harvest areas. The formulation allows for the use of exact solution methods such as mixed integer programming. The method was tested in a landscape consisting of 2821 stands. The application showed that large and compact harvest areas were created with limited sacrifice of financial value. The mixed integer programs were in most cases solved within a couple of hours. The method needs to be tested on different landscapes with different degrees of fragmentation. It is also necessary to evaluate the long term consequences of the large clear cuts that appear to be a consequence of this problem formulation.
  • Öhman, SLU, Department of Forest Resource Management, SE-901 83 Umeå, Sweden ORCID ID:E-mail: karin.ohman@srh.slu.se (email)
  • Eriksson, SLU, Department of Forest Resource Management, SE-901 83 Umeå, Sweden ORCID ID:E-mail:
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 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:

Category: Review article

article id 639, category Review article
Klaus von Gadow. (2000). Evaluating risk in forest planning models. Silva Fennica vol. 34 no. 2 article id 639. https://doi.org/10.14214/sf.639
The purpose of forest scenario modelling is to evaluate multiple management options and to answer what if questions relating to a particular development path of a given forest. Forest scenario planning can reduce uncertainty in management outcomes by anticipating the future in a systematic way, thus reducing the likelihood of unexpected events. It can also improve the chance that future developments will agree with specified objectives. Numerous techniques have been proposed for generating and evaluating scenarios of forest development. Some of the techniques are limited to applications in simple forest production systems while others are suitable for any type of forest management, including individual tree selection systems. Risk is defined as the expected loss due to a particular hazard for a given area and reference period. An expected loss may be calculated as the product of the damage and its probability. Risk analysis, risk evaluation and risk management are formal procedures for quantifying, evaluating and managing risk within a given hazard domain. Applications of risk analysis in forest scenario planning are rare and greater emphasis needs to be placed on hazard prediction. The aim of this contribution is to discuss some aspects of risk analysis, including examples of specific modelling tools. In a forest planning model risk can be considered in the form of specific constraints limiting the total risk in a given time period. Expected hazards can be used to exclude certain risky alternatives and finally, risk can be calculated and used to reduce the value of an objective function coefficient.
  • Gadow, Georg-August-University Göttingen, Institute for Forest Management, Büsgenweg 5, 37077 Göttingen, Germany ORCID ID:E-mail: kgadow@gwdg.de (email)

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