IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i13p9966-d1177220.html

Association Rule Mining-Based Generalized Growth Mode Selection: Maximizing the Value of Retired Mechanical Parts

Author

Listed:
  • Yuyao Guo

    (Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Wuhan 430081, China)

  • Lei Wang

    (Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Wuhan 430081, China
    Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China)

  • Zelin Zhang

    (Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China)

  • Jianhua Cao

    (Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Wuhan 430081, China)

  • Xuhui Xia

    (Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Wuhan 430081, China
    Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China)

Abstract

Due to the inability to restore the original performance, a significant portion of retired mechanical products is often replaced with new ones and discarded or recycled as low-value materials. This practice leads to energy waste and a decline in their residual value. The generalized growth remanufacturing model (GGRM) presents opportunities to enhance the residual value of retired products and parts. It achieves this by incorporating a broader range of growth modes compared to traditional restorative remanufacturing approaches. The selection of the growth mode is a crucial step to achieve GGRM. However, there is a limited number of growth mode selection methods that are specifically suitable for GGRM. The capacity and efficiency of the method are also significant factors to consider. Therefore, we propose a growth mode selection method based on association rule mining. This method consists of three main steps: Firstly, the ReliefF method is used to select the core failure characteristics of retired parts. Secondly, a genetic algorithm (GA) is employed to identify the association between core failure characteristics, repair technology, and maximum recoverability. Finally, based on the maximum recoverability, the appropriate growth mode is selected for each retired part. We conduct a case study on retired automobile universal transmission, and the results demonstrate the feasibility, efficiency, and accuracy of the proposed method.

Suggested Citation

  • Yuyao Guo & Lei Wang & Zelin Zhang & Jianhua Cao & Xuhui Xia, 2023. "Association Rule Mining-Based Generalized Growth Mode Selection: Maximizing the Value of Retired Mechanical Parts," Sustainability, MDPI, vol. 15(13), pages 1-20, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:9966-:d:1177220
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/13/9966/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/13/9966/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xiaohua Han & Ying Shen & Yiwen Bian, 2020. "Optimal recovery strategy of manufacturers: Remanufacturing products or recycling materials?," Annals of Operations Research, Springer, vol. 290(1), pages 463-489, July.
    2. Yande Gong & Mengze Chen & Yuliang Zhuang, 2019. "Decision-Making and Performance Analysis of Closed-Loop Supply Chain under Different Recycling Modes and Channel Power Structures," Sustainability, MDPI, vol. 11(22), pages 1-26, November.
    3. Liu, Conghu & Cai, Wei & Dinolov, Ognyan & Zhang, Cuixia & Rao, Weizhen & Jia, Shun & Li, Li & Chan, Felix T.S., 2018. "Emergy based sustainability evaluation of remanufacturing machining systems," Energy, Elsevier, vol. 150(C), pages 670-680.
    4. Seo, Wonchul & Yoon, Janghyeok & Park, Hyunseok & Coh, Byoung-youl & Lee, Jae-Min & Kwon, Oh-Jin, 2016. "Product opportunity identification based on internal capabilities using text mining and association rule mining," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 94-104.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lei Wang & Yunke Qi & Yuyao Guo & Zelin Zhang & Xuhui Xia, 2025. "A Method for Resolving Gene Mutation Conflicts of Retired Mechanical Parts: Generalized Remanufacturing Scheme Design Oriented Toward Resource Reutilization," Sustainability, MDPI, vol. 17(11), pages 1-27, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhen-Yu Chen & Xin-Li Liu & Li-Ping Yin, 2023. "Data-driven product configuration improvement and product line restructuring with text mining and multitask learning," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 2043-2059, April.
    2. Jinzhu Zhang & Wenqian Yu, 2020. "Early detection of technology opportunity based on analogy design and phrase semantic representation," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 551-576, October.
    3. Chenkai Xu & Jufang Bao, 2025. "Research on power battery closed-loop supply chain recycling decision considering advertising input under deposit policy," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 27(10), pages 24367-24395, October.
    4. Ren, Haiying & Zhao, Yuhui, 2021. "Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks," Technovation, Elsevier, vol. 101(C).
    5. Wu, Yingwen & Ji, Yangjian, 2023. "Identifying firm-specific technology opportunities from the perspective of competitors by using association rule mining," Journal of Informetrics, Elsevier, vol. 17(2).
    6. Shang, Zhendong & Gao, Dong & Jiang, Zhipeng & Lu, Yong, 2019. "Towards less energy intensive heavy-duty machine tools: Power consumption characteristics and energy-saving strategies," Energy, Elsevier, vol. 178(C), pages 263-276.
    7. Wei Lu & Jie Wu & Xiang Ji, 2025. "Consumer environmental preference information sharing with green manufacturer’s short video platform-selling," Annals of Operations Research, Springer, vol. 355(2), pages 2199-2221, December.
    8. Liu, Hongda & Huang, Feipeng & Huang, Jialiang, 2022. "Measuring the coordination decision of renewable energy as a natural resource contracts based on rights structure and corporate social responsibility from economic recovery," Resources Policy, Elsevier, vol. 78(C).
    9. Byeongki Jeong & Janghyeok Yoon, 2017. "Competitive Intelligence Analysis of Augmented Reality Technology Using Patent Information," Sustainability, MDPI, vol. 9(4), pages 1-22, March.
    10. Tianle Tian & Chuiyong Zheng & Liguo Yang & Xiaochun Luo & Lin Lu, 2022. "Optimal Recycling Channel Selection of Power Battery Closed-Loop Supply Chain Considering Corporate Social Responsibility in China," Sustainability, MDPI, vol. 14(24), pages 1-30, December.
    11. Dou, Guowei & Choi, Tsan-Ming, 2021. "Does implementing trade-in and green technology together benefit the environment?," European Journal of Operational Research, Elsevier, vol. 295(2), pages 517-533.
    12. Xuxin Lai & Nengmin Wang & Bin Jiang & Tao Jia, 2024. "Choosing Recovery Strategies for Waste Electronics: How Product Modularity Influences Cooperation and Competition," Sustainability, MDPI, vol. 16(20), pages 1-31, October.
    13. Hilal Shams & Altaf Hossain Molla & Mohd Nizam Ab Rahman & Hawa Hishamuddin & Zambri Harun & Nallapaneni Manoj Kumar, 2023. "Exploring Industry-Specific Research Themes on E-Waste: A Literature Review," Sustainability, MDPI, vol. 15(16), pages 1-22, August.
    14. Jiajia Nie & Qijun Wang & Gendao Li & Dun Liu, 2023. "To share or not to share? When information sharing meets remanufacturing," Annals of Operations Research, Springer, vol. 329(1), pages 815-846, October.
    15. Gao, Mengdi & Liu, Conghu & Li, Lei & Li, Qiang & Wang, Qingyang & Liu, Zhifeng, 2024. "Emergy-based method for the sustainability assessment and improvement of additive manufacturing systems," Energy, Elsevier, vol. 290(C).
    16. Dooho Lee, 2020. "Who Drives Green Innovation? A Game Theoretical Analysis of a Closed-Loop Supply Chain under Different Power Structures," IJERPH, MDPI, vol. 17(7), pages 1-26, March.
    17. Cai, Wei & Lai, Kee-hung, 2021. "Sustainability assessment of mechanical manufacturing systems in the industrial sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    18. Jian Wang & Wenxuan Shao, 2021. "Joint Capacity Investment, Collecting and Pricing Decisions in a Capacity Constraint Closed-Loop Supply Chain Considering Cooperation," Sustainability, MDPI, vol. 13(16), pages 1-28, August.
    19. Seo, Wonchul & Afifuddin, Mokh, 2024. "Developing a supervised learning model for anticipating potential technology convergence between technology topics," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    20. Li, Xin & Wu, Yundi & Cheng, Haolun & Xie, Qianqian & Daim, Tugrul, 2023. "Identifying technology opportunity using SAO semantic mining and outlier detection method: A case of triboelectric nanogenerator technology," Technological Forecasting and Social Change, Elsevier, vol. 189(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:9966-:d:1177220. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.