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Manufacturing Process Innovation-Oriented Knowledge Evaluation Using MCDM and Fuzzy Linguistic Computing in an Open Innovation Environment

Author

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  • Gangfeng Wang

    (Key Laboratory of Road Construction Technology and Equipment of MOE, Chang’an University, Xi’an 710064, China
    Sinomach Changlin Company Limited, Changzhou 213136, China)

  • Xitian Tian

    (Institute of CAPP & Manufacturing Engineering Software, Northwestern Polytechnical University, Xi’an 710072, China)

  • Yongbiao Hu

    (Key Laboratory of Road Construction Technology and Equipment of MOE, Chang’an University, Xi’an 710064, China)

  • Richard David Evans

    (Business Information Management and Operations, University of Westminster, London NW1 5LS, UK)

  • Mingrui Tian

    (Key Laboratory of Road Construction Technology and Equipment of MOE, Chang’an University, Xi’an 710064, China)

  • Rong Wang

    (Department of Information Engineering, Engineering University of CAPF, Xi’an 710086, China)

Abstract

In today’s complex, constantly evolving and innovation-supporting manufacturing systems, knowledge plays a vital role in sustainable manufacturing process planning and problem-solving, especially in the case of Computer-Aided Process Innovation (CAPI). To obtain formalized and promising process innovation knowledge under the open innovation paradigm, it is necessary to evaluate candidate knowledge and encourage improvement suggestions based on actual innovation situations. This paper proposes a process innovation-oriented knowledge evaluation approach using Multi-Criteria Decision-Making (MCDM) and fuzzy linguistic computing. Firstly, a comprehensive hierarchy evaluation index system for process innovation knowledge is designed. Secondly, by combining an analytic hierarchy process with fuzzy linguistic computing, a comprehensive criteria weighting determination method is applied to effectively aggregate the evaluation of criteria weights for each criterion and corresponding sub-criteria. Furthermore, fuzzy linguistic evaluations of performance ratings for each criterion and corresponding sub-criteria are calculated. Thus, a process innovation knowledge comprehensive value can be determined. Finally, an illustrative example of knowledge capture, evaluation and knowledge-inspired process problem solving for micro-turbine machining is presented to demonstrate the applicability of the proposed approach. It is expected that our model would lay the foundation for knowledge-driven CAPI in sustainable manufacturing.

Suggested Citation

  • Gangfeng Wang & Xitian Tian & Yongbiao Hu & Richard David Evans & Mingrui Tian & Rong Wang, 2017. "Manufacturing Process Innovation-Oriented Knowledge Evaluation Using MCDM and Fuzzy Linguistic Computing in an Open Innovation Environment," Sustainability, MDPI, vol. 9(9), pages 1-19, September.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:9:p:1630-:d:111856
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    References listed on IDEAS

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    Cited by:

    1. Kao-Yi Shen & Gwo-Hshiung Tzeng, 2018. "Advances in Multiple Criteria Decision Making for Sustainability: Modeling and Applications," Sustainability, MDPI, vol. 10(5), pages 1-7, May.
    2. Gajdzik, Bożena & Gawlik, Remigiusz, 2018. "Choosing the Production Function Model for an Optimal Measurement of the Restructuring Efficiency of the Polish Metallurgical Sector in Years 2000–2015," MPRA Paper 83618, University Library of Munich, Germany.
    3. Sanja Puzović & Jasmina Vesić Vasović & Dragan D. Milanović & Vladan Paunović, 2023. "A Hybrid Fuzzy MCDM Approach to Open Innovation Partner Evaluation," Mathematics, MDPI, vol. 11(14), pages 1-26, July.
    4. Fanshun Zhang & Zhuorui Zhang & Quanquan Zhang & Xiaochun Zhu, 2023. "Dynamic Evaluation of Product Innovation Knowledge Concerning the Interactive Relationship between Innovative Subjects: A Multi-Objective Optimization Approach," Mathematics, MDPI, vol. 11(9), pages 1-33, April.

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