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Advances in Manufacturing

, Volume 7, Issue 4, pp 423–437 | Cite as

Nonempirical hybrid multi-attribute decision-making method for design for remanufacturing

  • Qing-Shan Gong
  • Hua Zhang
  • Zhi-Gang JiangEmail author
  • Han Wang
  • Yan Wang
  • Xiao-Li Hu
Article
  • 69 Downloads

Abstract

Design for remanufacturing (DfRem) is the process of considering remanufacturing characteristics during product design in order to reduce the number of issues during the remanufacturing stage. This decision-making in DfRem is influenced by the designers’ subjective preferences owing to a lack of explicitly defined remanufacturing knowledge for designers, which can lead to indecisive design schemes. In order to objectively select the optimal design scheme for remanufacturing, a nonempirical hybrid multi-attribute decision-making method is presented to alleviate the impacts of subjective factors. In this method, design characteristics and demand information are characterized through the matter-element theory. Coupled with design principles, some initial design schemes are proposed. Evaluation criteria are established considering the technical, economic, and environmental factors. The entropy weight and vague set are used to determine the optimal design scheme via a multi-attribute decision-making approach. The design of a bearing assembly machine for remanufacturing is taken as an example to illustrate the practicality and validity of the proposed method. The results revealed that the proposed method was effective in the decision-making of DfRem.

Keywords

Design for remanufacturing (DfRem) Remanufacturing Multi-attribute decision-making Vague set Entropy weight 

Notes

Acknowledgements

The work described in this paper was supported by the Plateau Disciplines in Shanghai, the National Natural Science Foundation of China (Grant No. 51675388), the Educational Commission of Hubei Province (Grant No. Q20171804), and the Key Laboratory of Automotive Power Train and Electronics (Grant No. ZDK1201802). These financial contributions are gratefully acknowledged.

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Copyright information

© Shanghai University and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Hubei Key Laboratory of Mechanical Transmission and Manufacturing EngineeringWuhan University of Science & TechnologyWuhanPeople’s Republic of China
  2. 2.Key Laboratory of Automotive Power Train and ElectronicsHubei University of Automotive TechnologyShiyanPeople’s Republic of China
  3. 3.Key Laboratory of Metallurgical Equipment and Control TechnologyWuhan University of Science & TechnologyWuhanPeople’s Republic of China
  4. 4.School of Computing, Engineering and MathematicsUniversity of BrightonBrightonUK
  5. 5.School of Mechanical EngineeringShanghai Dianji UniversityShanghaiPeople’s Republic of China

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