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Smart recovery decision-making of used industrial equipment for sustainable manufacturing: belt lifter case study

Author

Listed:
  • Kai Meng

    (Massachusetts Institute of Technology)

  • Xiaoming Qian

    (Nanjing University of Aeronautics and Astronautics
    Jiangsu Key Laboratory of Precision and Micro-Manufacturing Technology)

  • Peihuang Lou

    (Nanjing University of Aeronautics and Astronautics
    Jiangsu Key Laboratory of Precision and Micro-Manufacturing Technology)

  • Jiong Zhang

    (Miracle Automation Engineering Co., LTD)

Abstract

End-of-Life (EOL) product recovery is proved to be an attractive way to achieve sustainable manufacturing while extending the producer’s responsibility to closed-loop product service. However, it is still a challenge to provide flexible and smart recovery plans for industrial equipment at different periods of product service. In this paper, we investigate the smart recovery decision-making problem. We propose a system framework for the implementation of smart EOL management based on product condition monitoring. Different product-level EOL business strategies and component-level recovery options are suggested in this recovery decision support system. Then, multi-objective optimization models are formulated to identify the age-dependent recovery roadmap that best matches the product condition and meets the business goals. In order to achieve environmentally friendly recovery, both recovery profits and energy performances are optimized in our models. We conduct a case study of belt lifter used in the automobile assembly line. The Non-dominated Sorting Genetic Algorithm II is used to solve the proposed model. Numerical experiments validate our models and provide practical insights into flexible recovery business.

Suggested Citation

  • Kai Meng & Xiaoming Qian & Peihuang Lou & Jiong Zhang, 2020. "Smart recovery decision-making of used industrial equipment for sustainable manufacturing: belt lifter case study," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 183-197, January.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:1:d:10.1007_s10845-018-1439-2
    DOI: 10.1007/s10845-018-1439-2
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    References listed on IDEAS

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    1. Meng, Kai & Lou, Peihuang & Peng, Xianghui & Prybutok, Victor, 2017. "Multi-objective optimization decision-making of quality dependent product recovery for sustainability," International Journal of Production Economics, Elsevier, vol. 188(C), pages 72-85.
    2. Niknejad, A. & Petrovic, D., 2014. "Optimisation of integrated reverse logistics networks with different product recovery routes," European Journal of Operational Research, Elsevier, vol. 238(1), pages 143-154.
    3. Guo, Shanshan & Aydin, Goker & Souza, Gilvan C., 2014. "Dismantle or remanufacture?," European Journal of Operational Research, Elsevier, vol. 233(3), pages 580-583.
    4. S. H. R. Torabi & S. Alibabaei & B. Barooghi Bonab & M. H. Sadeghi & Gh. Faraji, 2017. "Design and optimization of turbine blade preform forging using RSM and NSGA II," Journal of Intelligent Manufacturing, Springer, vol. 28(6), pages 1409-1419, August.
    5. Dekker, Rommert & Bloemhof, Jacqueline & Mallidis, Ioannis, 2012. "Operations Research for green logistics – An overview of aspects, issues, contributions and challenges," European Journal of Operational Research, Elsevier, vol. 219(3), pages 671-679.
    6. Kai Ding & Pingyu Jiang & Mei Zheng, 2017. "Environmental and economic sustainability-aware resource service scheduling for industrial product service systems," Journal of Intelligent Manufacturing, Springer, vol. 28(6), pages 1303-1316, August.
    7. Kai Meng & Peihuang Lou & Xianghui Peng & Victor Prybutok, 2017. "Quality-driven recovery decisions for used components in reverse logistics," International Journal of Production Research, Taylor & Francis Journals, vol. 55(16), pages 4712-4728, August.
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    Cited by:

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    4. Małgorzata Jasiulewicz-Kaczmarek & Patryk Żywica & Arkadiusz Gola, 2021. "Fuzzy set theory driven maintenance sustainability performance assessment model: a multiple criteria approach," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1497-1515, June.

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