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A random intuitionistic fuzzy factor analysis model for complex multi-attribute large group decision-making in dynamic environments

Author

Listed:
  • Xiaohong Chen

    (Central South University
    Hunan University of Technology and Business
    Hunan University of Technology and Business)

  • Mengjing Wu

    (Central South University)

  • Chunqiao Tan

    (Nanjing Audit University)

  • Tao Zhang

    (Loughborough University London)

Abstract

The challenge of complex multi-attribute large group decision-making (CMALGDM) is reflected from three perspectives: interrelated attributes, large group decision makers (DMs) and dynamic decision environments. However, there are few decision techniques that can address the three perspectives simultaneously. This paper proposes a random intuitionistic fuzzy factor analysis model, aiming to address the challenge of CMALGDM from the three perspectives. The proposed method effectively reduces the dimensionality of the original data and takes into account the underlying random environmental factors which may affect the performances of alternatives. The development of this method follows three steps. First, the random intuitionistic fuzzy variables are developed to deal with a hybrid uncertain situation where fuzziness and randomness co-exist. Second, a novel factor analysis model for random intuitionistic fuzzy variables is proposed. This model uses specific mappings or functions to define the way in which evaluations are affected by the dynamic environment vector through data learning or prior distributions. Third, multiple correlated attribute variables and DM variables are transformed into fewer independent factors by a two-step procedure using the proposed model. In addition, the objective classifications and weights for attributes and DMs are obtained from the results of orthogonal rotated factor loading. An illustrative case and detailed comparisons of decision results in different environmental conditions are demonstrated to test the feasibility and validity of the proposed method.

Suggested Citation

  • Xiaohong Chen & Mengjing Wu & Chunqiao Tan & Tao Zhang, 2021. "A random intuitionistic fuzzy factor analysis model for complex multi-attribute large group decision-making in dynamic environments," Fuzzy Optimization and Decision Making, Springer, vol. 20(1), pages 101-127, March.
    • Handle: RePEc:spr:fuzodm:v:20:y:2021:i:1:d:10.1007_s10700-020-09334-9
    DOI: 10.1007/s10700-020-09334-9
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    References listed on IDEAS

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    1. Zhi Pei, 2017. "Multi-attribute decision making based on a novel IF point operator," Fuzzy Optimization and Decision Making, Springer, vol. 16(4), pages 505-524, December.
    2. Xunjie Gou & Zeshui Xu, 2017. "Exponential operations for intuitionistic fuzzy numbers and interval numbers in multi-attribute decision making," Fuzzy Optimization and Decision Making, Springer, vol. 16(2), pages 183-204, June.
    3. Li, Jun & Xu, Jiuping, 2009. "A novel portfolio selection model in a hybrid uncertain environment," Omega, Elsevier, vol. 37(2), pages 439-449, April.
    4. Liu, Bingsheng & Shen, Yinghua & Zhang, Wei & Chen, Xiaohong & Wang, Xueqing, 2015. "An interval-valued intuitionistic fuzzy principal component analysis model-based method for complex multi-attribute large-group decision-making," European Journal of Operational Research, Elsevier, vol. 245(1), pages 209-225.
    5. Xiaohong Chen & Hui Li & Chunqiao Tan, 2019. "An intuitionstic fuzzy factorial analysis model for multi-attribute decision-making under random environment," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(1), pages 81-100, January.
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