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Forest management models and combinatorial algorithms: analysis of state of the art

Author

Listed:
  • Andres Weintraub
  • Richard Church
  • Alan Murray
  • Monique Guignard

Abstract

Linear Programming and Mixed Integer Linear Programs have been used for forest planning since the 60's to support decision making on forest harvesting and management. In particular, during the last two decades of forest management there has been an increased interest in spatial issues. Further, new environmental concerns, such as resource sustainability and wildlife protection, impose that increased attention be paid to activities carried out on the ground. Road building needed for access also requires spatial definiton. As a result, more complex models must be used. We discuss the issues which have led to the combinatorial nature of some main forest management problems and the solution algorithms that have been proposed for these problems, including local search heuristics, random search approaches, strengthening of mixed integer model formulations and Lagrangian relaxation. In this survey, we discuss which of the proposed approaches have been used succesfully, the advantages and shortcomings of each and what are still open research problems. Copyright Kluwer Academic Publishers 2000

Suggested Citation

  • Andres Weintraub & Richard Church & Alan Murray & Monique Guignard, 2000. "Forest management models and combinatorial algorithms: analysis of state of the art," Annals of Operations Research, Springer, vol. 96(1), pages 271-285, November.
    • Handle: RePEc:spr:annopr:v:96:y:2000:i:1:p:271-285:10.1023/a:1018991116559
    DOI: 10.1023/A:1018991116559
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    Citations

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    Cited by:

    1. Ran Wei & Alan Murray, 2015. "Spatial uncertainty in harvest scheduling," Annals of Operations Research, Springer, vol. 232(1), pages 275-289, September.
    2. Minas, James P. & Hearne, John W. & Martell, David L., 2014. "A spatial optimisation model for multi-period landscape level fuel management to mitigate wildfire impacts," European Journal of Operational Research, Elsevier, vol. 232(2), pages 412-422.
    3. Álvarez-Miranda, Eduardo & Garcia-Gonzalo, Jordi & Pais, Cristobal & Weintraub, Andrés, 2019. "A multicriteria stochastic optimization framework for sustainable forest decision making under uncertainty," Forest Policy and Economics, Elsevier, vol. 103(C), pages 112-122.

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