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Identification of Critical Components of Complex Product Based on Hybrid Intuitionistic Fuzzy Set and Improved Mahalanobis-Taguchi System

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Abstract

To avoid the decrease of system reliability due to insufficient component maintenance and the resource waste caused by excessive component maintenance, identifying the critical components of complex products is an effective way to improve the efficiency of maintenance activities. Existing studies on identifying critical components of complex products are mainly from two aspects i.e., topological properties and functional properties, respectively. In this paper, we combine these two aspects to establish a hybrid intuitionistic fuzzy set to incorporate the different types of attribute values. Considering the mutual correlation between attributes, a combination of AHP (Analytic Hierarchy Process) and Improved Mahalanobis-Taguchi System (MTS) is used to determine the λ-Shapley fuzzy measures for attributes. Then, the λ-Shapley Choquet integral intuitionistic fuzzy TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method is proposed for calculating the closeness degrees of components to generate their ranking order. Finally, a case study which is about the right gear of airbus 320 is taken as an example to verify the feasibility and effectiveness of this method. This novel methodology can effectively solve the critical components identification problem with different types of evaluation information and completely unknown weight information of attributes, which provides the implementation of protection measures for the system reliability of complex products.

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Acknowledgments

The work was supported by National Natural Science Foundation of China under Grant Nos.71471146, 71501158 and 71871182; General Program of Humanities and Social Sciences Research of Ministry of Education of China under Grant No. 20XJA630003; Fundamental Research Funds for the Central Universities under Grant No.3102020JC06 and Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University under Grant No.CX2021095. The authors would like to thank the referees for their efforts to improve the quality of this paper.

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Authors and Affiliations

  1. School of Management, Northwestern Polytechnical University, Xi’an, 710072, China

    Naiding Yang, Mingzhen Zhang & Fangmei Wangdu

  2. School of Economics and Management, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Ruimeng Li

Authors
  1. Naiding Yang
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  2. Mingzhen Zhang
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  3. Fangmei Wangdu
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  4. Ruimeng Li
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Corresponding author

Correspondence to Naiding Yang.

Additional information

Naiding Yang is a professor and doctoral mentor of School of Management, Northwestern Polytechnical University, Xi’an, China. His research interests include management system engineering, risk management, decision analysis. He is the executive director of Emergency Management Institute of Northwestern Polytechnical University, and a member of Systems Engineering Society of China, Management Science and Engineering Society of China, Project Management Research Committee China, etc.

Mingzhen Zhang is a Ph.D. candidate of School of Management, Northwestern Polytechnical University, Xi’an, China. Her research interests include complex product systems, decision analysis and evolutionary game.

Fangmei Wangdu is a Ph.D. candidate of School of Management, Northwestern Polytechnical University, Xi’an, China. Her research interests include complex products, knowledge heterogeneity, innovation performance.

Ruimeng Li is a lecturer in School of Economics and Management, Chongqing University of Posts and Telecommunications, Chongqing, China. She received her PHD in management science and engineering from Northwestern Polytechnical University. Her research interests are management system engineering and project risk management.

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Yang, N., Zhang, M., Wangdu, F. et al. Identification of Critical Components of Complex Product Based on Hybrid Intuitionistic Fuzzy Set and Improved Mahalanobis-Taguchi System. J. Syst. Sci. Syst. Eng. 30, 533–551 (2021). https://doi.org/10.1007/s11518-021-5503-7

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