Articles containing the keyword 'heuristic'

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, E-mail: tp@mm.unknown
• Kangas, E-mail: jk@mm.unknown
• Kniivilä, E-mail: mk@mm.unknown
• Tiainen, E-mail: at@mm.unknown

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, E-mail: jk@mm.unknown
• Loikkanen, E-mail: tl@mm.unknown
• Pukkala, E-mail: tp@mm.unknown
• Pykäläinen, E-mail: jp@mm.unknown

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 E-mail: farrell0503@126.com
• Bettinger, Warnell School of Forestry and Natural Resources, University of Georgia, Athens 30602, GA, USA E-mail: pbettinger@warnell.uga.edu
• Liu, Department of Forest Management, College of Forestry, Northeast Forestry University, Harbin 150040, China E-mail: lzg19700602@163.com
• Qin, Department of Forestry Economic, College of Economic & Management, Northeast Forestry University, Harbin 150040, China E-mail: huiyanqin@hotmail.com
• Zhao, Department of Forest Management, College of Forestry, Northeast Forestry University, Harbin 150040, China E-mail: zyinghui0925@126.com

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 E-mail: jbachmatiuk@isa.ulisboa.pt
• Garcia-Gonzalo, Centro de Estudos Florestais, Instituto Superior de Agronomia, Universidade de Lisboa, Lisboa, Portugal E-mail: jordigarcia@isa.ulisboa.pt
• Borges, Centro de Estudos Florestais, Instituto Superior de Agronomia, Universidade de Lisboa, Lisboa, Portugal E-mail: joseborges@isa.ulisboa.pt

• Bettinger, School of Forestry and Natural Resources, 180 E. Green Street, University of Georgia, Athens, Georgia, USA 30602 E-mail: pbettinger@warnell.uga.edu
• Demirci, General Directorate of Forestry, Ministry of Forest and Water Affairs, Republic of Turkey E-mail: mehmetdemirci@yahoo.com
• Boston, Department of Forest Engineering, Resources and Management, College of Forestry, Oregon State University, USA E-mail: Kevin.Boston@oregonstate.edu

• Zhu, Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA E-mail: jz@nn.us
• Bettinger, Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA E-mail: pbettinger@warnell.uga.edu
• Li, Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA E-mail: rl@nn.us

• Zeng, University of Joensuu, Faculty of Forest Sciences, P. O. Box 111, FI-80101 Joensuu, Finland E-mail: hongcheng.zeng@joensuu.fi
• Pukkala, University of Joensuu, Faculty of Forest Sciences, P. O. Box 111, FI-80101 Joensuu, Finland E-mail: tp@nn.fi
• Peltola, University of Joensuu, Faculty of Forest Sciences, P. O. Box 111, FI-80101 Joensuu, Finland E-mail: hp@nn.fi
• Kellomäki, University of Joensuu, Faculty of Forest Sciences, P. O. Box 111, FI-80101 Joensuu, Finland E-mail: sk@nn.fi

• Heinonen, University of Joensuu, Faculty of Forestry, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: th@nn.fi
• Pukkala, University of Joensuu, Faculty of Forestry, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: timo.pukkala@joensuu.fi

• Boston, Forest Fibre Solutions, Carter Holt Harvey, Tokoroa, New Zealand E-mail: kevin.boston@chh.co.nz
• Bettinger, Department of Forest Resources, Oregon State University, Corvallis, OR 97331 E-mail: pb@nn.us

• Daume, The University of Edinburgh, Institute for Representation and Reasoning, Division of Informatics, 80 South Bridge, Edinburgh EH1 1HN, United Kingdom E-mail: stefand@dai.ed.ac.uk
• Robertson, The University of Edinburgh, Institute for Representation and Reasoning, Division of Informatics, 80 South Bridge, Edinburgh EH1 1HN, United Kingdom E-mail: dr@nn.uk