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


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.


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



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.


  1. 1.
    Cai W, Liu C, Zhang C et al (2018) Developing the ecological compensation criterion of industrial solid waste based on energy for sustainable development. Energy 157:940–948Google Scholar
  2. 2.
    Zhu S, Jiang Z, Zhang H et al (2017) A carbon efficiency evaluation method for manufacturing process chain decision-making. J Clean Prod 148:665–680Google Scholar
  3. 3.
    Feng Y, Zhang Z, Tian G et al (2018) Data-driven accurate design of variable blank holder force in sheet forming under interval uncertainty using sequential approximate multi-objective optimization. Futur Gener Comput Syst 86:1242–1250Google Scholar
  4. 4.
    Wang H, Jiang Z, Wang Y et al (2018) A two-stage optimization method for energy-saving flexible job-shop scheduling based on energy dynamic characterization. J Clean Prod 188:575–588Google Scholar
  5. 5.
    Cai W, Lai K, Liu C et al (2019) Promoting sustainability of manufacturing industry through the lean energy-saving and emission-reduction strategy. Sci Total Environ 665:23–32Google Scholar
  6. 6.
    Jiang ZG, Zhang H, Sutherland JW (2011) Development of multi-criteria decision making model for remanufacturing technology portfolio selection. J Clean Prod 19:1939–1945Google Scholar
  7. 7.
    Tian G, Ren Y, Feng Y et al (2019) Modeling and planning for dual-objective selective disassembly using AND/OR graph and discrete artificial bee colony. IEEE Trans Ind Inform 15(4):2456–2468Google Scholar
  8. 8.
    Gong Q, Zhang H, Jiang Z et al (2018) Methodology for steel plate remanufacturing cleaning with flexible cable impact contact and friction. Procedia CIRP 72:1374–1379Google Scholar
  9. 9.
    Tian G, Zhou M, Li P (2018) Disassembly sequence planning considering fuzzy component quality and varying operational cost. IEEE Trans Autom Sci Eng 15:748–760Google Scholar
  10. 10.
    Jiang Z, Jiang Y, Wang Y et al (2019) A hybrid approach of rough set and case-based reasoning to remanufacturing process planning. J Intell Manuf 30:19–32Google Scholar
  11. 11.
    Wang H, Jiang Z, Zhang X et al (2017) A fault feature characterization based method for remanufacturing process planning optimization. J Clean Prod 161:708–719Google Scholar
  12. 12.
    Wang H, Jiang Z, Zhang H et al (2019) An integrated MCDM approach considering demands-matching for reverse logistics. J Clean Prod 208:199–210Google Scholar
  13. 13.
    Goepp V, Zwolinski P, Caillaud E (2014) Design process and data models to support the design of sustainable remanufactured products. Comput Ind 65(3):480–490Google Scholar
  14. 14.
    Ding Z, Jiang Z, Zhang H et al (2018) An integrated decision-making method for selecting machine tool guide ways considering remanufacture ability. Int J Comput Integr Manuf. CrossRefGoogle Scholar
  15. 15.
    Cao HJ, Chen X, Chen HF (2012) The redesign method of heavy machine tool based on matter-element theory and its application. Manuf Technol Mach Tool 12:38–43Google Scholar
  16. 16.
    Abdulrahman MDA, Subramanian N, Liu C et al (2015) Viability of remanufacturing practice: a strategic decision making framework for Chinese auto-parts companies. J Clean Prod 105:311–323Google Scholar
  17. 17.
    Yang SS, Ong SK, Nee AYC (2016) Adecision support tool for product design for remanufacturing. Procedia CIRP 40:144–149Google Scholar
  18. 18.
    Tian G, Zhang H, Zhou MC (2017) AHP, gray correlation, and TOPSIS combined approach to green performance evaluation of design alternatives. IEEE Trans Syst Man Cybern Syst 48(7):1093–1105Google Scholar
  19. 19.
    Charter M, Gray C (2008) Remanufacturing and product design. Int J Prod Dev 6(34):375–392Google Scholar
  20. 20.
    Windmill J, Hatcher GD, Ijomah WL (2013) Integrating design for remanufacture into the design process: the operational factors. J Clean Prod 39:200–208Google Scholar
  21. 21.
    Hatcher GD, Ijomah WL, Windmill JFC (2011) Design for remanufacture: a literature review and future research needs. J Clean Prod 19(17/18):2004–2014Google Scholar
  22. 22.
    Fegade V, Shrivatsava RL, Kale AV (2015) Design for remanufacturing: methods and their approaches. Mater Today Proc 2(4/5):1849–1858Google Scholar
  23. 23.
    Li L, Li C, Tang Y (2017) An integrated approach of reverse engineering aided remanufacturing process for worn components. Robot Comput Integr Manuf 48:39–50Google Scholar
  24. 24.
    Xing K, Belusko M, Luong L (2007) An evaluation model of product upgradeability for remanufacture. Int J Adv Manuf Technol 35(1/2):1–14Google Scholar
  25. 25.
    Shi J, Fan S, Wang Y (2018) A GHG emissions analysis method for product remanufacturing: a case study on a diesel engine. J Clean Prod. CrossRefGoogle Scholar
  26. 26.
    Du Y, Cao H, Liu F (2012) An integrated method for evaluating the remanufacture ability of used machine tool. J Clean Prod 20(1):82–91Google Scholar
  27. 27.
    Sundin E (2004) Product and process design for successful remanufacturing. Linköping University Electronic Press, LinköpingGoogle Scholar
  28. 28.
    Gehin A, Zwolinski P, Brissaud D (2008) A tool to implement sustainable end-of-life strategies in the product development phase. J Clean Prod 16(5):566–576Google Scholar
  29. 29.
    Harivardhini S, Krishna KM, Chakrabarti A (2017) An integrated framework for supporting decision making during early design stages on end-of-life disassembly. J Clean Prod 168:558–574Google Scholar
  30. 30.
    Smith S, Hsu LY, Smith GC (2016) Partial disassembly sequence planning based on cost-benefit analysis. J Clean Prod 139:729–739Google Scholar
  31. 31.
    Cai W, Liu C, Lai K et al (2019) Energy performance certification in mechanical manufacturing industry: a review and analysis. Energy Convers Manag 186:415–432Google Scholar
  32. 32.
    de Aguiar J, Oliveira LD, da Silva JO (2016) A design tool to diagnose product recyclability during product design phase. J Clean Prod 141:219–229Google Scholar
  33. 33.
    Chang JC, Graves SC, Kirchain RE (2018) Integrated planning for design and production in two-stage recycling operations. Eur J Oper Res 273(2):535–547MathSciNetzbMATHGoogle Scholar
  34. 34.
    Dostatni E, Diakun J, Grajewski D (2016) Multi-agent system to support decision-making process in design for recycling. Soft Comput 20(11):4347–4361Google Scholar
  35. 35.
    Peng G, Yu H, Liu X et al (2010) A desktop virtual reality-based integrated system for complex product maintainability design and verification. Assem Autom 30(4):333–344Google Scholar
  36. 36.
    Zhou D, Chen J, Lv C et al (2016) A method for integrating ergonomics analysis into maintainability design in a virtual environment. Int J Ind Ergon 54:154–163Google Scholar
  37. 37.
    Kumar S, Khan IA, Gandhi OP (2015) A theoretical framework for extraction and quantification of psychological attributes in design for maintainability: a team-inspired approach. Res Eng Des 26(4):1–20Google Scholar
  38. 38.
    Feng Y, Hong Z, Tian G et al (2018) Environmentally friendly MCDM of reliability-based product optimisation combining DEMATEL-based ANP, interval uncertainty and VlseKriterijumska Optimizacija Kompromisno Resenje (VIKOR). Inf Sci 442:128–144Google Scholar
  39. 39.
    Golinska P, Kosacka M, Mierzwiak R (2015) Grey decision making as a tool for the classification of the sustainability level of remanufacturing companies. J Clean Prod 105:28–40Google Scholar
  40. 40.
    Yang SS, Nasr N, Ong SK (2017) Designing automotive products for remanufacturing from material selection perspective. J Clean Prod 153:570–579Google Scholar
  41. 41.
    Wang H, Jiang Z, Wang Y et al (2018) A demands-matching multi-criteria decision-making method for reverse logistics. Procedia CIRP 72:1398–1403Google Scholar
  42. 42.
    Peng S, Li T, Li M (2019) An integrated decision model of restoring technologies selection for engine remanufacturing practice. J Clean Prod 206:598–610Google Scholar
  43. 43.
    Feng YX, Zhou MC, Tian GD et al (2018) Target disassembly sequencing and scheme evaluation for CNC machine tools using improved multiobjective ant colony algorithm and fuzzy integral. IEEE Trans Syst Man Cybern Syst. CrossRefGoogle Scholar
  44. 44.
    Bhatia MS, Srivastava RK (2018) Analysis of external barriers to remanufacturing using grey-DEMATEL approach: an Indian perspective. Resour Conserv Recycl 136:79–87Google Scholar
  45. 45.
    Zhao QQ, Huang TM (2018) Multi-objective decision making based on entropy weighted-Vague sets. J Comput Appl 38(5):1250–1253Google Scholar
  46. 46.
    Na S, Zhang YZ, Shen GX et al (2011) Reliability evaluation of CNC machine tools based on matter-element model and extensional evaluation method. In: 2011 international conference on mechatronic science, electric engineering and computer (MEC), 19–22 Aug, Jilin, pp 2345–2348Google Scholar
  47. 47.
    Liu GF, Liu ZF, Li G (2001) Green design and green manufacturing. China Machine Press, BeijingGoogle Scholar
  48. 48.
    Jiang ZG, Ding ZY, Zhang H et al (2019) Data-driven ecological performance evaluation for remanufacturing process. Energy Convers Manag. CrossRefGoogle Scholar
  49. 49.
    Schroeder NB (2011) Design of a second life product family from the perspective of the remanufacturing agent. University of Illinois, Urbana-ChampaignGoogle Scholar
  50. 50.
    Duraccio V, Compagno L, Trapani N et al (2016) Failure prevention through performance evaluation of reliability components in working condition. J Fail Anal Prev 16:1092–1100Google Scholar
  51. 51.
    Li L, Huang H, Zhao F et al (2019) Variations of energy demand with process parameters in cylindrical drawing of stainless steel. J Manuf Sci Eng. CrossRefGoogle Scholar
  52. 52.
    Yan T, Han C (2014) A novel approach of rough conditional entropy-based attribute selection for incomplete decision system. Math Probl Eng 5:1–15Google Scholar
  53. 53.
    Wang J, Liu SY, Zhang J (2005) Fuzzy multiple objectives decision making based on vague sets. Syst Eng Theory Pract 25(2):119–122Google Scholar
  54. 54.
    Elzarka HM, Yan H, Chakraborty D (2017) A vague set fuzzy multi-attribute group decision-making model for selecting onsite renewable energy technologies for institutional owners of constructed facilities. Sustain Cities Soc 35:430–439Google Scholar

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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