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Deriving crisp and consistent priorities for fuzzy AHP-based multicriteria systems using non-linear constrained optimization

Author

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  • Raman Kumar Goyal

    (Panjab University)

  • Sakshi Kaushal

    (Panjab University)

Abstract

Fuzzy optimization models are used to derive crisp weights (priority vectors) for the fuzzy analytic hierarchy process (AHP) based multicriteria decision making systems. These optimization models deal with the imprecise judgements of decision makers by formulating the optimization problem as the system of constrained non linear equations. Firstly, a Genetic Algorithm based heuristic solution for this optimization problem is implemented in this paper. It has been found that the crisp weights derived from this solution for fuzzy-AHP system, sometimes lead to less consistent or inconsistent solutions. To deal with this problem, we have proposed a consistency based constraint for the optimization models. A decision maker can set the consistency threshold value and if the solution exists for that threshold value then crisp weights can be derived, otherwise it can be concluded that the fuzzy comparison matrix for AHP is not consistent for the given threshold. Three examples are considered to demonstrate the effectiveness of the proposed method. Results with the proposed constraint based fuzzy optimization model are more consistent than the existing optimization models.

Suggested Citation

  • Raman Kumar Goyal & Sakshi Kaushal, 2018. "Deriving crisp and consistent priorities for fuzzy AHP-based multicriteria systems using non-linear constrained optimization," Fuzzy Optimization and Decision Making, Springer, vol. 17(2), pages 195-209, June.
    • Handle: RePEc:spr:fuzodm:v:17:y:2018:i:2:d:10.1007_s10700-017-9267-y
    DOI: 10.1007/s10700-017-9267-y
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    References listed on IDEAS

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    Cited by:

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    3. Jie Tang & Fanyong Meng & Francisco Javier Cabrerizo & Enrique Herrera-Viedma, 2019. "A procedure for group decision making with interval-valued intuitionistic linguistic fuzzy preference relations," Fuzzy Optimization and Decision Making, Springer, vol. 18(4), pages 493-527, December.

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