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A Mass-Customization-Based Remanufacturing Scheme Design Method for Used Products

Author

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  • Wei Zhou

    (School of Environmental Science and Engineering, Hubei Polytechnic University, Huangshi 435003, China)

  • Chao Ke

    (School of Automotive Technology and Services, Wuhan City Polytechnic, Wuhan 430070, China
    Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China)

Abstract

Remanufacturing scheme design (RSD) is an essential step in the restoration and upgrading of used products. However, the quantity of remanufactured products is growing rapidly, and customers have personalized demands for remanufactured products that lead to shorter design cycles. In addition, the used products are scrapped due to their own defects, such as performance failure and functional degradation, which correspond to the inherent remanufacturing demand (IRD) of used products. Faced with large quantities of used products, how to quickly develop reasonable remanufacturing schemes for satisfying customers’ individual demands and the IRD is an urgent problem to be solved. To address these issues, a mass customization-based RSD method is proposed. First, remanufacturing demand comprising customer demand and the IRD is analyzed to determine the RSD targets and remanufacturing types. Then, the RSD methods are intelligently selected based on the remanufacturing types, which include restorative remanufacturing, upgrade remanufacturing and hybrid remanufacturing, while the hybrid contains restorative remanufacturing and upgrade remanufacturing. Moreover, the restorative remanufacturing scheme is generated to satisfy the restorative remanufacturing targets based on reverse engineering (RE) and the tool contact point path section line (TCPPSL) method. After used products are restored, case-based reasoning (CBR) is used to retrieve the case that best matches the upgrade remanufacturing targets, while the grey relational analysis (GRA) algorithm is applied to calculate the similarity between cases. Finally, the feasibility of this method is verified by considering the RSD of a used lathe. The results indicated that the proposed approach can rapidly help designers to obtain remanufacturing solutions for satisfying the customer demand and IRD.

Suggested Citation

  • Wei Zhou & Chao Ke, 2022. "A Mass-Customization-Based Remanufacturing Scheme Design Method for Used Products," Sustainability, MDPI, vol. 14(16), pages 1-22, August.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:10059-:d:887931
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    References listed on IDEAS

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    1. Sahand Somi & Nima Gerami Seresht & Aminah Robinson Fayek, 2020. "Framework for Risk Identification of Renewable Energy Projects Using Fuzzy Case-Based Reasoning," Sustainability, MDPI, vol. 12(13), pages 1-11, June.
    2. Da Silveira, Giovani & Borenstein, Denis & Fogliatto, Flavio S., 2001. "Mass customization: Literature review and research directions," International Journal of Production Economics, Elsevier, vol. 72(1), pages 1-13, June.
    3. Zhigang Jiang & Ya Jiang & Yan Wang & Hua Zhang & Huajun Cao & Guangdong Tian, 2019. "A hybrid approach of rough set and case-based reasoning to remanufacturing process planning," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 19-32, January.
    4. Shwetank Avikal & Rohit Singh & Rashmi Rashmi, 2020. "QFD and Fuzzy Kano model based approach for classification of aesthetic attributes of SUV car profile," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 271-284, February.
    5. Zhang, Min & Guo, Hangfei & Huo, Baofeng & Zhao, Xiande & Huang, Jianbo, 2019. "Linking supply chain quality integration with mass customization and product modularity," International Journal of Production Economics, Elsevier, vol. 207(C), pages 227-235.
    6. Sonu Rajak & P. Parthiban & R. Dhanalakshmi, 2018. "Selection of Transportation Channels in Closed-Loop Supply Chain Using Meta-Heuristic Algorithm," International Journal of Information Systems and Supply Chain Management (IJISSCM), IGI Global, vol. 11(3), pages 64-86, July.
    Full references (including those not matched with items on IDEAS)

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