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A Heuristic System to Solve Mixed Integér Forest Planning Models

Author

Listed:
  • Andrés Weintraub

    (University of Chile, Santiago, Chile)

  • Greg Jones

    (USDA Forest Service, Missoula, Montana)

  • Adrian Magendzo

    (University of Chile, Santiago, Chile)

  • Mary Meacham

    (USDA Forest Service, Missoula, Montana)

  • Malcolm Kirby

    (USDA Forest Service (retired), Berkeley, California)

Abstract

Implementing forest plans involves decisions regarding the geographic arrangement and timing of management activities, and the design of efficient transportation networks to provide access for these activities. These planning problems have been modeled successfully as mixed integer linear programs. However, these models are difficult to solve with commercial software due to their relatively large number of binary variables. We present a heuristic approach that has solved these problems well. The heuristic rules and a linear programming package interact within a closed system requiring no special user intervention. We describe several applications of this system for decision making in the USDA Forest Service.

Suggested Citation

  • Andrés Weintraub & Greg Jones & Adrian Magendzo & Mary Meacham & Malcolm Kirby, 1994. "A Heuristic System to Solve Mixed Integér Forest Planning Models," Operations Research, INFORMS, vol. 42(6), pages 1010-1024, December.
    • Handle: RePEc:inm:oropre:v:42:y:1994:i:6:p:1010-1024
    DOI: 10.1287/opre.42.6.1010
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    Citations

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    Cited by:

    1. Andrés Weintraub & Carlos Romero, 2006. "Operations Research Models and the Management of Agricultural and Forestry Resources: A Review and Comparison," Interfaces, INFORMS, vol. 36(5), pages 446-457, October.
    2. Sinha, Ankur & Rämö, Janne & Malo, Pekka & Kallio, Markku & Tahvonen, Olli, 2017. "Optimal management of naturally regenerating uneven-aged forests," European Journal of Operational Research, Elsevier, vol. 256(3), pages 886-900.
    3. Jiehong Kong & Mikael Rönnqvist & Mikael Frisk, 2015. "Using mixed integer programming models to synchronously determine production levels and market prices in an integrated market for roundwood and forest biomass," Annals of Operations Research, Springer, vol. 232(1), pages 179-199, September.
    4. Olsson, Leif & Lohmander, Peter, 2005. "Optimal forest transportation with respect to road investments," Forest Policy and Economics, Elsevier, vol. 7(3), pages 369-379, March.
    5. Andres Weintraub P., 2007. "Integer programming in forestry," Annals of Operations Research, Springer, vol. 149(1), pages 209-216, February.
    6. Olsson, Leif, 2005. "Road investment scenarios in Northern Sweden," Forest Policy and Economics, Elsevier, vol. 7(4), pages 615-623, May.
    7. Nader Naderializadeh & Kevin A. Crowe, 2020. "Formulating the integrated forest harvest-scheduling model to reduce the cost of the road-networks," Operational Research, Springer, vol. 20(4), pages 2283-2306, December.
    8. Rafael Epstein & Ramiro Morales & Jorge Serón & Andres Weintraub, 1999. "Use of OR Systems in the Chilean Forest Industries," Interfaces, INFORMS, vol. 29(1), pages 7-29, February.
    9. Tiernan, Dermot & Nieuwenhuis, Maarten, 2005. "Financial optimisation of forest-level harvest scheduling in Ireland - A case study," Journal of Forest Economics, Elsevier, vol. 11(1), pages 21-43, June.
    10. Fernando Veliz & Jean-Paul Watson & Andres Weintraub & Roger Wets & David Woodruff, 2015. "Stochastic optimization models in forest planning: a progressive hedging solution approach," Annals of Operations Research, Springer, vol. 232(1), pages 259-274, September.
    11. Nicolas Andalaft & Pablo Andalaft & Monique Guignard & Adrian Magendzo & Alexis Wainer & Andres Weintraub, 2003. "A Problem of Forest Harvesting and Road Building Solved Through Model Strengthening and Lagrangean Relaxation," Operations Research, INFORMS, vol. 51(4), pages 613-628, August.
    12. Martell, David L. & Gunn, Eldon A. & Weintraub, Andres, 1998. "Forest management challenges for operational researchers," European Journal of Operational Research, Elsevier, vol. 104(1), pages 1-17, January.

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