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Numerical scales generated individually for analytic hierarchy process

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  • Dong, Yucheng
  • Hong, Wei-Chiang
  • Xu, Yinfeng
  • Yu, Shui

Abstract

Because individual interpretations of the analytic hierarchy process (AHP) linguistic scale vary for each user, this study proposes a novel framework that AHP decision makers can use to generate numerical scales individually, based on the 2-tuple linguistic modeling of AHP scale problems. By using the concept of transitive calibration, individual characteristics in understanding the AHP linguistic scale are first defined. An algorithm is then proposed for detecting the individual characteristics from the linguistic pairwise comparison data that is associated with each of the AHP individual decision makers. Finally, a nonlinear programming model is proposed to generate individual numerical scales that optimally match the obtained individual characteristics. Two well-known numerical examples are re-examined using the proposed framework to demonstrate its validity.

Suggested Citation

  • Dong, Yucheng & Hong, Wei-Chiang & Xu, Yinfeng & Yu, Shui, 2013. "Numerical scales generated individually for analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 229(3), pages 654-662.
    • Handle: RePEc:eee:ejores:v:229:y:2013:i:3:p:654-662
    DOI: 10.1016/j.ejor.2013.03.019
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    References listed on IDEAS

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    Cited by:

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    2. Yuji Sato & Kim Hua Tan, 2023. "Inconsistency indices in pairwise comparisons: an improvement of the Consistency Index," Annals of Operations Research, Springer, vol. 326(2), pages 809-830, July.
    3. Vijay Pereira & Umesh Bamel, 2023. "Charting the managerial and theoretical evolutionary path of AHP using thematic and systematic review: a decadal (2012–2021) study," Annals of Operations Research, Springer, vol. 326(2), pages 635-651, July.
    4. Zhu, Bin & Xu, Zeshui, 2014. "Analytic hierarchy process-hesitant group decision making," European Journal of Operational Research, Elsevier, vol. 239(3), pages 794-801.
    5. Li, Cong-Cong & Dong, Yucheng & Liang, Haiming & Pedrycz, Witold & Herrera, Francisco, 2022. "Data-driven method to learning personalized individual semantics to support linguistic multi-attribute decision making," Omega, Elsevier, vol. 111(C).
    6. Xia, Meimei & Chen, Jian, 2015. "Multi-criteria group decision making based on bilateral agreements," European Journal of Operational Research, Elsevier, vol. 240(3), pages 756-764.
    7. Ho, William & Ma, Xin, 2018. "The state-of-the-art integrations and applications of the analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 267(2), pages 399-414.
    8. Wu, Xingli & Liao, Huchang, 2019. "A consensus-based probabilistic linguistic gained and lost dominance score method," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1017-1027.

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