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Direct spatial optimization in natural resource management: Four linear programming examples

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  • John Hof
  • Michael Bevers

Abstract

This paper discusses methods for directly incorporating spatial relationships in natural resource optimization models. The approaches are contrasted with the conventional emphasis on adjacency restrictions. Four formulations are presented that address a variety of problems using linear (continuous-variable) programming, which allows reliable solvability of even very large models. The problems addressed include: habitat placement for the black-footed ferret (an aggressive disperser), habitat placement for the black-tailed prairie dog (a gregarious, recalcitrant disperser), placement of control efforts directed at restricting the spread of an exotic pest, and the semi-permanent placement of habitat for the northern spotted owl (a static problem). Copyright Kluwer Academic Publishers 2000

Suggested Citation

  • John Hof & Michael Bevers, 2000. "Direct spatial optimization in natural resource management: Four linear programming examples," Annals of Operations Research, Springer, vol. 95(1), pages 67-81, January.
    • Handle: RePEc:spr:annopr:v:95:y:2000:i:1:p:67-81:10.1023/a:1018902124311
    DOI: 10.1023/A:1018902124311
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    Cited by:

    1. Pablo Vanegas & Dirk Cattrysse & Anja Wijffels & Jos Orshoven, 2014. "Compactness and flow minimization requirements in reforestation initiatives: a heuristic solution method," Annals of Operations Research, Springer, vol. 219(1), pages 433-456, August.
    2. İ. Esra Büyüktahtakın & Robert G. Haight, 2018. "A review of operations research models in invasive species management: state of the art, challenges, and future directions," Annals of Operations Research, Springer, vol. 271(2), pages 357-403, December.
    3. Punt, Maarten J. & Groeneveld, Rolf A. & van Ierland, Ekko C. & Stel, Jan H., 2009. "Spatial planning of offshore wind farms: A windfall to marine environmental protection?," Ecological Economics, Elsevier, vol. 69(1), pages 93-103, November.
    4. Eyyüb Y. Kıbış & İ. Esra Büyüktahtakın & Robert G. Haight & Najmaddin Akhundov & Kathleen Knight & Charles E. Flower, 2021. "A Multistage Stochastic Programming Approach to the Optimal Surveillance and Control of the Emerald Ash Borer in Cities," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 808-834, May.
    5. Maqsood, Imran & Huang, Guo H. & Scott Yeomans, Julian, 2005. "An interval-parameter fuzzy two-stage stochastic program for water resources management under uncertainty," European Journal of Operational Research, Elsevier, vol. 167(1), pages 208-225, November.
    6. Carlos García-Alonso & Leonor Pérez-Naranjo & Juan Fernández-Caballero, 2014. "Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms," Annals of Operations Research, Springer, vol. 219(1), pages 187-202, August.

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