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A Product Evolution Rules Based Method for Retired Mechanical Product Demand Acquisition

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  • Wenbin Zhou

    (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)

  • Lei Wang

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

  • Zelin Zhang

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

  • Baotong Chen

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

Abstract

Accurate acquisition of retired mechanical products demand (RMPD) is the basis for realizing effective utilization of remanufacturing service data and improving the feasibility of remanufacturing schemes. Some studies have explored product demands, making product demands an important support for product design and development. However, these studies are obtained through the transformation of customer and market demand information, and few studies are studied from a product perspective. However, remanufacturing services for retired mechanical products (RMP) must consider the impact of the failure characteristics. Consequently, based on the generalized growth of RMP driven by the failure characteristics, the concept of RMPD is proposed in this paper. Then, the improved ant colony algorithm is proposed to mine the generalized growth evolution law of RMP from the empirical data of remanufacturing services, and the RMPD is deduced based on the mapping relationship between the product and its attributes. Finally, the feasibility and applicability of the proposed method are verified by obtaining the demand for retired rolls. In detail, the results show that the proposed method can obtain the RMPD accurately and efficiently, and the performance of the method can be continuously optimized with the accumulation of empirical data.

Suggested Citation

  • Wenbin Zhou & Xuhui Xia & Lei Wang & Zelin Zhang & Baotong Chen, 2022. "A Product Evolution Rules Based Method for Retired Mechanical Product Demand Acquisition," Sustainability, MDPI, vol. 14(23), pages 1-17, November.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:15701-:d:984024
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    References listed on IDEAS

    as
    1. Seunghyun Oh & Jaewoong Choi & Namuk Ko & Janghyeok Yoon, 2020. "Predicting product development directions for new product planning using patent classification-based link prediction," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1833-1876, December.
    2. Wei Song & Shuailei Yuan & Yun Yang & Chufeng He, 2022. "A Study of Community Group Purchasing Vehicle Routing Problems Considering Service Time Windows," Sustainability, MDPI, vol. 14(12), pages 1-17, June.
    3. Alencar Bravo & Darli Vieira & Thais Ayres Rebello, 2022. "The Origins, Evolution, Current State, and Future of Green Products and Consumer Research: A Bibliometric Analysis," Sustainability, MDPI, vol. 14(17), pages 1-25, September.
    4. Biswas, Sumana & Ali, Ismail & Chakrabortty, Ripon K. & Turan, Hasan Hüseyin & Elsawah, Sondoss & Ryan, Michael J., 2022. "Dynamic modeling for product family evolution combined with artificial neural network based forecasting model: A study of iPhone evolution," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    5. Jiseong Noh & Hyun-Ji Park & Jong Soo Kim & Seung-June Hwang, 2020. "Gated Recurrent Unit with Genetic Algorithm for Product Demand Forecasting in Supply Chain Management," Mathematics, MDPI, vol. 8(4), pages 1-14, April.
    6. Rosario Domingo & Sergio Aguado, 2015. "Overall Environmental Equipment Effectiveness as a Metric of a Lean and Green Manufacturing System," Sustainability, MDPI, vol. 7(7), pages 1-17, July.
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