Using Tabu search to schedule timber harvests subject to spatial wildlife goals for big game

Bettinger, Pete; Sessions, John; Boston, Kevin

doi:10.1016/s0304-3800(96)00007-5

Cited by 127 publications

(63 citation statements)

References 4 publications

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“…Simulated Annealing (Kirkpatrick et al, 1983;Cerny, 1985;Yoshimoto et al, 1994;Murray and Church, 1995;Bettinger et al, 1997;Öhman and Eriksson, 1998). Die Methode basiert auf einer Analogie mit der Abkühlung und Kristallisation flüssiger Stoffe und ist in zahlreichen…”

Section: Eine Heuristische Methode Zur Lösung Dieser Art Forstlicher unclassified

Forsteinrichtung

Gadow¹

2006

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Section: Eine Heuristische Methode Zur Lösung Dieser Art Forstlicher unclassified

Forsteinrichtung

Gadow¹

2006

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“…Mathematical programming techniques, such as Linear Programming (LP), are currently used to generate optimal solutions in large forest areas without spatial constraints, while Integer Programming (IP) and Mixed Integer Programming (MIP) have been proposed to solve THP models in smaller forest areas that incorporate spatial constraints. Meta-heuristic optimization techniques have been used for larger sized problems incorporating several spatial constraints; these techniques include Monte Carlo Programming (MCP), Tabu Search (TS), Simulated Annealing (SA), Threshold Acceptance (TA) and Genetic Algorithm (GA) (Lockwood and Moore, 1993;Bettinger et al, 1997;Richards and Gunn, 2000;Caro et al, 2003;Ohman and Eriksson, 2002;Lu and Eriksson, 2000;Liu et al, 2006;Daniel et al, 2012).…”

Section: Related Workmentioning

confidence: 99%

Optimization Model of Selective Cutting for Timber Harvest Planning Based on a Simulated Annealing Approach

Abdullah¹,

Abdullah²,

Hamdan³

et al. 2014

Journal of Computer Science

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An optimization model for Timber Harvest Planning (THP) is used to form harvest areas with the objective of maximizing the tree harvest volume subject to harvest regulations. Most optimization models for THP currently in use are based on clear cutting and restricted to adjacency constraints to prevent the creation of large clear-cut openings in the forest. However, an optimization model based on selective cuttingthe cutting technique utilized in tropical countries-has rarely been described in the literature. The aim of this study was to propose an optimization model for THP based on selective cutting and subject to a maximum number of trees to be harvested and a minimum number of trees to be damaged during each planning period. The model was solved using three optimization techniques to identify the most suitable technique for use in the process of harvest area formation: Monte Carlo Programming (MCP), Simulated Annealing (SA) and Threshold Acceptance (TA). The obtained results indicate that the SA method provides better solutions than the MCP and TA methods, regardless of problem size. As a conclusion, the proposed model provides a tool to generate timber harvest plans with the improved monitoring and control techniques used in harvesting operations in tropical countries.

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“…However, the latter approach may also be computationally wasteful, since the choices made from the neighborhood are not necessarily feasible until the constraints have been assessed. One of the first tabu search papers in forest management that involved non-linear wildlife habitat goals was presented by Bettinger et al (1997). Here, a standard 1-opt tabu search process was used to maximize an even-flow timber harvest objective while also meeting constraint levels for the development and maintenance of Rocky Mountain elk (Cervus elaphus nelsoni) habitat.…”

Section: Incorporation Of Non-linear Goal Assessment Processesmentioning

confidence: 99%

“…Tabu search was initially developed by Glover (1989Glover ( , 1990, and has been applied to a number of forest management and planning problems (Murray & Church 1995;Bettinger et al 1997Bettinger et al , 1998Brumelle et al 1998). In general, when using tabu search to address forest management and planning problems, a number of forest plans are deterministically developed and assessed, each subsequent plan being slightly different than its predecessor, and thus each is considered an iteration of the modeling process.…”

Section: Introductionmentioning

confidence: 99%