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Personalized individual semantics-based approach for linguistic failure modes and effects analysis with incomplete preference information

Author

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  • Hengjie Zhang
  • Yucheng Dong
  • Jing Xiao
  • Francisco Chiclana
  • Enrique Herrera-Viedma

Abstract

Failure Modes and Effects Analysis (FMEA) is a very useful reliability-management instrument for detecting and mitigating risks in various fields. The linguistic assessment approach has recently been widely used in FMEA. Words mean different things to different people, so FMEA members may present Personalized Individual Semantics (PIS) in their linguistic assessment information. This article presents the design of a PIS-based FMEA approach, in which members express their opinions over failure modes and risk factors using Linguistic Distribution Assessment Matrices (LDAMs) and also provide their opinions over failure modes using incomplete Additive Preference Relations (APRs). A preference information preprocessing method with a two-stage optimization model is presented to generate complete APRs with acceptable consistency levels from incomplete APRs. Then, a deviation minimum-based optimization model is designed to personalize individual semantics by minimizing the deviation between APR and the numerical assessment matrix derived from the corresponding LDAM. This is followed by the development of a ranking process to generate the risk ordering of failure modes. A case study and a detailed comparison analysis are presented to show the effectiveness of the PIS-based linguistic FMEA approach.

Suggested Citation

  • Hengjie Zhang & Yucheng Dong & Jing Xiao & Francisco Chiclana & Enrique Herrera-Viedma, 2020. "Personalized individual semantics-based approach for linguistic failure modes and effects analysis with incomplete preference information," IISE Transactions, Taylor & Francis Journals, vol. 52(11), pages 1275-1296, November.
    • Handle: RePEc:taf:uiiexx:v:52:y:2020:i:11:p:1275-1296
    DOI: 10.1080/24725854.2020.1731774
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    Cited by:

    1. Mostafa Fadaeefath Abadi & Mohammad Hosseini Rahdar & Fuzhan Nasiri & Fariborz Haghighat, 2022. "Fault Identification and Fault Impact Analysis of The Vapor Compression Refrigeration Systems in Buildings: A System Reliability Approach," Energies, MDPI, vol. 15(16), pages 1-21, August.
    2. Zhang, Hengjie & Dong, Yucheng & Xiao, Jing & Chiclana, Francisco & Herrera-Viedma, Enrique, 2021. "Consensus and opinion evolution-based failure mode and effect analysis approach for reliability management in social network and uncertainty contexts," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    3. Kamilla Marchewka-Bartkowiak & Karolina Anna Nowak & Michał Litwiński, 2022. "Digital valuation of personality using personal tokens," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1555-1576, September.
    4. Li, Ying & Liu, Peide & Li, Gang, 2023. "An asymmetric cost consensus based failure mode and effect analysis method with personalized risk attitude information," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    5. Chuanhao Fan & Yan Chen & Yan Zhu & Long Zhang & Wenjuan Wu & Bin Ling & Sijie Tang, 2022. "Using Fuzzy Comprehensive Evaluation to Assess the Competency of Full-Time Water Conservancy Emergency Rescue Teams," Mathematics, MDPI, vol. 10(12), pages 1-25, June.
    6. Suvojit Dhara & Adrijit Goswami, 2023. "Causal Relationship and Ranking Technique (CRRT): A Novel Group Decision-Making Model and Application in Students’ Performance Assessment in Indian High School Context," Group Decision and Negotiation, Springer, vol. 32(4), pages 835-870, August.
    7. Peng Wu & Jinpei Liu & Ligang Zhou & Huayou Chen, 2022. "An Integrated Group Decision-Making Method with Hesitant Qualitative Information Based on DEA Cross-Efficiency and Priority Aggregation for Evaluating Factors Affecting a Resilient City," Group Decision and Negotiation, Springer, vol. 31(2), pages 293-316, April.
    8. 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).

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