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A group decision making model based on goal programming with fuzzy hierarchy: an application to regional forest planning

Author

Listed:
  • Amelia Bilbao-Terol

    (University of Oviedo)

  • Mariano Jiménez

    (University of the Basque Country)

  • Mar Arenas-Parra

    (University of Oviedo)

Abstract

In this paper a multi-criteria group decision making model is presented in which there is a heterogeneity among the decision makers due to their different expertise and/or their different level of political control. The relative importance of the decision makers in the group is handled in a soft manner using fuzzy relations. We suppose that each decision maker has his/her preferred solution, obtained by applying any of the techniques of distance-based multi-objective programming [compromise, goal programming (GP), goal programming with fuzzy hierarchy, etc.]. These solutions are used as aspiration levels in a group GP model in which the differences between the unwanted deviations are interpreted in terms of the degree of achievement of the relative importance amongst the group members. In this way, a group GP model with fuzzy hierarchy (Group-GPFH) is constructed. The solution for this model is proposed as a collective decision. To show the applicability of our proposal, a regional forest planning problem is addressed. The objective is to determine tree species composition in order to improve the values achieved by Pan-European indicators for sustainable forest management. This problem involves stakeholders with competing interests and different preference schemes for the aforementioned indicators. The application of our proposal to this problem allows us to be able to comfortably address all these issues. The results obtained are consistent with the preferences of each stakeholder and their hierarchy within the group.

Suggested Citation

  • Amelia Bilbao-Terol & Mariano Jiménez & Mar Arenas-Parra, 2016. "A group decision making model based on goal programming with fuzzy hierarchy: an application to regional forest planning," Annals of Operations Research, Springer, vol. 245(1), pages 137-162, October.
    • Handle: RePEc:spr:annopr:v:245:y:2016:i:1:d:10.1007_s10479-014-1633-3
    DOI: 10.1007/s10479-014-1633-3
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    Cited by:

    1. Jiménez, Mariano & Bilbao-Terol, Amelia & Arenas-Parra, Mar, 2021. "Incorporating preferential weights as a benchmark into a Sequential Reference Point Method," European Journal of Operational Research, Elsevier, vol. 291(2), pages 575-585.
    2. Yasaman Parsia & Shahryar Sorooshian, 2020. "A Decision-Making Algorithm for Rearchitecting of Healthcare Facilities to Minimize Nosocomial Infections Risks," IJERPH, MDPI, vol. 17(3), pages 1-20, January.
    3. Vassiliki Kazana & Angelos Kazaklis & Dimitrios Raptis & Christos Stamatiou, 2020. "A combined multi-criteria approach to assess forest management sustainability: an application to the forests of Eastern Macedonia & Thrace Region in Greece," Annals of Operations Research, Springer, vol. 294(1), pages 321-343, November.
    4. Mila Bravo & Dylan Jones & David Pla-Santamaria & Francisco Salas-Molina, 2022. "Encompassing statistically unquantifiable randomness in goal programming: an application to portfolio selection," Operational Research, Springer, vol. 22(5), pages 5685-5706, November.

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