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A hybrid approach of rough set and case-based reasoning to remanufacturing process planning

Author

Listed:
  • Zhigang Jiang

    (Wuhan University of Science and Technology)

  • Ya Jiang

    (Wuhan University of Science and Technology)

  • Yan Wang

    (University of Brighton)

  • Hua Zhang

    (Wuhan University of Science and Technology)

  • Huajun Cao

    (Chongqing University)

  • Guangdong Tian

    (Jilin University)

Abstract

Remanufacturing, a process returning used products to at least as good as new condition, is increasingly recognized as an important part of the circular economy. Since returned used components for remanufacturing have varying conditions and different defects, remanufacturing is very time-consuming and labor-intensive. There is an urgent need to reuse knowledge generated from existing parts remanufacturing to rapidly create sound process planning for the new arrival of used parts. A hybrid method combing rough set (RS) and cased-based reasoning (CBR) for remanufacturing process planning is presented in this paper. RS is employed for features reduction and rapid determination of features’ weights automatically, and CBR is utilized to calculate the similarity of process cases to identify the most suitable solution effectively from case database. The application of the methodology is demonstrated in an example of remanufacturing process for a saddle guide. The results indicated that the quality of remanufactured products has been improved significantly. The method has been implemented in a prototype system using Visual Studio 2010 and Microsoft SQL Server2008. The results suggested that the hybrid RS–CBR system is feasible and effective for the rapid generation of sound process planning for remanufacturing.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:1:d:10.1007_s10845-016-1231-0
    DOI: 10.1007/s10845-016-1231-0
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    References listed on IDEAS

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    1. V. Daniel R. Guide , Jr. & Gilvan C. Souza & Luk N. Van Wassenhove & Joseph D. Blackburn, 2006. "Time Value of Commercial Product Returns," Management Science, INFORMS, vol. 52(8), pages 1200-1214, August.
    2. Wen, Haijun & Liu, Mingzhou & Liu, Changyi & Liu, Conghu, 2015. "Remanufacturing production planning with compensation function approximation method," Applied Mathematics and Computation, Elsevier, vol. 256(C), pages 742-753.
    3. Teunter, Ruud H. & Flapper, Simme Douwe P., 2011. "Optimal core acquisition and remanufacturing policies under uncertain core quality fractions," European Journal of Operational Research, Elsevier, vol. 210(2), pages 241-248, April.
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    Cited by:

    1. Jiyoung Jung & Kundo Park & Byungjin Cho & Jinkyoo Park & Seunghwa Ryu, 2023. "Optimization of injection molding process using multi-objective bayesian optimization and constrained generative inverse design networks," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3623-3636, December.
    2. Qihao Liu & Xinyu Li & Liang Gao, 2021. "Mathematical modeling and a hybrid evolutionary algorithm for process planning," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 781-797, March.
    3. Longhua Xu & Chuanzhen Huang & Chengwu Li & Jun Wang & Hanlian Liu & Xiaodan Wang, 2021. "An improved case based reasoning method and its application in estimation of surface quality toward intelligent machining," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 313-327, January.
    4. Mohammad Reza Khosravani & Sara Nasiri, 2020. "Injection molding manufacturing process: review of case-based reasoning applications," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 847-864, April.
    5. Wenjie Wang & Guangdong Tian & Gang Yuan & Duc Truong Pham, 2023. "Energy-time tradeoffs for remanufacturing system scheduling using an invasive weed optimization algorithm," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1065-1083, March.
    6. Shengrui Yu & Tianfeng Zhang & Yun Zhang & Zhigao Huang & Huang Gao & Wen Han & Lih-Sheng Turng & Huamin Zhou, 2022. "Intelligent setting of process parameters for injection molding based on case-based reasoning of molding features," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 77-89, January.
    7. Rui Wang & Xiangyu Guo & Shisheng Zhong & Gaolei Peng & Lin Wang, 2022. "Decision rule mining for machining method chains based on rough set theory," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 799-807, March.
    8. Chao Ke & Xiuyan Pan & Pan Wan & Zixi Huang & Zhigang Jiang, 2023. "An Intelligent Redesign Method for Used Products Based on Digital Twin," Sustainability, MDPI, vol. 15(12), pages 1-19, June.
    9. Yanbin Du & Guohua He & Bo Li & Zhijie Zhou & Guoao Wu, 2022. "In-service machine tool remanufacturing: a sustainable resource-saving and high-valued recovery approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(1), pages 1335-1358, January.
    10. Wenkang Zhang & Yufan Zheng & Rafiq Ahmad, 2023. "The integrated process planning and scheduling of flexible job-shop-type remanufacturing systems using improved artificial bee colony algorithm," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 2963-2988, October.
    11. 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.

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