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Knowledge recommendation for product development using integrated rough set-information entropy correction

Author

Listed:
  • Zhenyong Wu

    (Guangxi University
    National University of Singapore)

  • Lina He

    (Southwest Jiaotong University)

  • Yuan Wang

    (National University of Singapore)

  • Mark Goh

    (National University of Singapore)

  • Xinguo Ming

    (Shanghai Jiao Tong University)

Abstract

New product development is knowledge intensive as it needs the work teams and design engineers located at various locations to constantly share, update, and re-use knowledge. As such, improving the efficiency of acquiring knowledge and coping with the challenge of frequently retrieving related knowledge have become a key factor to managing knowledge in new product development. This paper combines rough set theory and information entropy to establish a new knowledge recommender technique to address the issue of knowledge reuse for new product development. Our method enhances knowledge acquisition and reuse, as it provides a realistic framework for knowledge acquisition and reuse, encompassing the entire process from what the design and work teams need, to recommending what they should have. To validate the proposed approach, we perform experiments on a case study to demonstrate the benefit and performance.

Suggested Citation

  • Zhenyong Wu & Lina He & Yuan Wang & Mark Goh & Xinguo Ming, 2020. "Knowledge recommendation for product development using integrated rough set-information entropy correction," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1559-1578, August.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:6:d:10.1007_s10845-020-01534-9
    DOI: 10.1007/s10845-020-01534-9
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    References listed on IDEAS

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    1. Peter Chhim & Ratna Babu Chinnam & Noureddin Sadawi, 2019. "Product design and manufacturing process based ontology for manufacturing knowledge reuse," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 905-916, February.
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    6. Ruben Costa & Celson Lima & João Sarraipa & Ricardo Jardim-Gonçalves, 2016. "Facilitating knowledge sharing and reuse in building and construction domain: an ontology-based approach," Journal of Intelligent Manufacturing, Springer, vol. 27(1), pages 263-282, February.
    7. Yaping Fu & Hongfeng Wang & Guangdong Tian & Zhiwu Li & Hesuan Hu, 2019. "Two-agent stochastic flow shop deteriorating scheduling via a hybrid multi-objective evolutionary algorithm," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2257-2272, June.
    8. Kai-Qing Zhou & Li-Ping Mo & Jie Jin & Azlan Mohd Zain, 2019. "An equivalent generating algorithm to model fuzzy Petri net for knowledge-based system," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1831-1842, April.
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    Cited by:

    1. Chen-Fu Chien & Hsin-Jung Wu, 2024. "Integrated circuit probe card troubleshooting based on rough set theory for advanced quality control and an empirical study," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 275-287, January.
    2. Yongjun Ji & Zuhua Jiang & Xinyu Li & Yongwen Huang & Fuhua Wang, 2023. "A multitask context-aware approach for design lesson-learned knowledge recommendation in collaborative product design," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1615-1637, April.

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