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Evaluation and Selection of the Quality Methods for Manufacturing Process Reliability Improvement—Intuitionistic Fuzzy Sets and Genetic Algorithm Approach

Author

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  • Ranka Gojković

    (Faculty of Mechanical Engineering, University of East Sarajevo, 71123 East Sarajevo, Bosnia and Herzegovina)

  • Goran Đurić

    (Faculty of Mechanical Engineering, University of Belgrade, 11120 Belgrade, Serbia)

  • Danijela Tadić

    (Faculty of Engineering, University of Kragujevac, 34000 Kragujevac, Serbia)

  • Snežana Nestić

    (Faculty of Engineering, University of Kragujevac, 34000 Kragujevac, Serbia)

  • Aleksandar Aleksić

    (Faculty of Engineering, University of Kragujevac, 34000 Kragujevac, Serbia)

Abstract

The aim of this research is to propose a hybrid decision-making model for evaluation and selection of quality methods whose application leads to improved reliability of manufacturing in the process industry. Evaluation of failures and determination of their priorities are based on failure mode and effect analysis (FMEA), which is a widely used framework in practice combining with triangular intuitionistic fuzzy numbers (TIFNs). The all-existing uncertainties in the relative importance of the risk factors (RFs), their values, applicability of the quality methods, as well as implementation costs are described by pre-defined linguistic terms which are modeled by the TIFNs. The selection of quality methods is stated as the rubber knapsack problem which is decomposed into subproblems with a certain number of solution elements. The solution of this problem is found by using genetic algorithm (GA). The model is verified through the case study with the real-life data originating from a significant number of organizations from one region. It is shown that the proposed model is highly suitable as a decision-making tool for improving the manufacturing process reliability in small and medium enterprises (SMEs) of process industry.

Suggested Citation

  • Ranka Gojković & Goran Đurić & Danijela Tadić & Snežana Nestić & Aleksandar Aleksić, 2021. "Evaluation and Selection of the Quality Methods for Manufacturing Process Reliability Improvement—Intuitionistic Fuzzy Sets and Genetic Algorithm Approach," Mathematics, MDPI, vol. 9(13), pages 1-17, June.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:13:p:1531-:d:584867
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    References listed on IDEAS

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    1. Huang, Jia & You, Jian-Xin & Liu, Hu-Chen & Song, Ming-Shun, 2020. "Failure mode and effect analysis improvement: A systematic literature review and future research agenda," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    2. Hans Kellerer & Ulrich Pferschy, 2004. "Improved Dynamic Programming in Connection with an FPTAS for the Knapsack Problem," Journal of Combinatorial Optimization, Springer, vol. 8(1), pages 5-11, March.
    3. Absalom E Ezugwu & Francis Akutsah & Micheal O Olusanya & Aderemi O Adewumi, 2018. "Enhanced intelligent water drops algorithm for multi-depot vehicle routing problem," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-32, March.
    4. Liu, Hu-Chen & You, Jian-Xin & Duan, Chun-Yan, 2019. "An integrated approach for failure mode and effect analysis under interval-valued intuitionistic fuzzy environment," International Journal of Production Economics, Elsevier, vol. 207(C), pages 163-172.
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