Skip to main content

End-of-Life Product Recovery Optimization of Disassembled Parts Based on Collaborative Decision-Making

  • Conference paper
  • First Online:
Smart and Sustainable Collaborative Networks 4.0 (PRO-VE 2021)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 629))

Included in the following conference series:

Abstract

Greenhouse gas emissions are a major problem for the environment. One of the vital activities to reduce the emissions is including the circular economy (CE) approaches like reuse and remanufacture in disassembled products to recovering End-of-life products. In this paper, we consider CE in the disassembly of products not only to reduce CO2 emissions but also to reducing cost and improving fairness among operators. To obtain this goal, collaborative decision-making with three decision-makers (DMs) is considered to set sustainability via choosing the best EOL recovery options in the disassembly of products. Industrial managers, human resource managers, and environmental managers are three decision-makers who will collaborate to improve three indicators, which are cost, setting fairness among operators, and reducing CO2 emissions. To implement this collaboration, a mixed-integer multi-objective mathematical model is proposed and solved by Ɛ-constraint. According to the results, DMs can select the best recovery options of parts to have a trade-off among indicators.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. United Nations (2015). https://sustainabledevelopment.un.org/topics/climatechange. Accessed 30 Apr 2018

  2. Kokubu, K., Itsubo, N., Nakajima, M., Yamada, T.: Low Carbon Supply Chain Management. Chuokeizai-sha Inc., Tokyo (2015)

    Google Scholar 

  3. Ilgin, M.A., Gupta, S.M.: Remanufacturing Modeling and Analysis. CRC Press, Boca Raton (2012)

    Google Scholar 

  4. Hasegawa, S., Kinoshita, Y., Yamada, T., Bracke, S.: Life cycle option selection of disassembly parts for material-based CO2 saving rate and recovery cost: analysis of different market value and labor cost for reused parts in German and Japanese cases. Int. J. Prod. Econ. 213, 229–242 (2019)

    Article  Google Scholar 

  5. Inoue, M., et al.: Quantitative estimate of CO2 emission reduction from reuse of automobile parts in Japan. Int. J. Supply Chain Manag. 6(4), 110–117 (2017)

    Google Scholar 

  6. Lambert, A.J.D., Gupta, S.M.: Disassembly Modeling for Assembly, Maintenance Reuse, and Recycling. CRC Press, Boca Raton (2005)

    MATH  Google Scholar 

  7. European Commission (2015). https://ec.europa.eu/clima/policies/international/negotiations/paris_en. Accessed 10 May 2018

  8. Ren, Y., Tian, G., Zhao, F., Yu, D., Zhang, C.: Selective cooperative disassembly planning based on multi-objective discrete artificial bee colony algorithm. Eng. Appl. Artif. Intell. 64, 415–431 (2017)

    Article  Google Scholar 

  9. Wang, H., Penga, Q., Zhangb, J., Gub, P.: Selective disassembly planning for the end-of-life product. Procedia CIRP 60, 512–517 (2017)

    Article  Google Scholar 

  10. Igarashi, K., Yamada, T., Gupta, S.M., Inoue, M., Itsubo, N.: Disassembly system modeling and design with parts selection for cost, recycling and CO2 saving rates using multi criteria optimization. J. Manuf. Syst. 38, 151–164 (2016)

    Article  Google Scholar 

  11. Smith, S., Hsu, L.Y., Smith, G.C.: Partial disassembly sequence planning based on cost-benefit analysis. J. Clean. Prod. 139, 729–739 (2016)

    Article  Google Scholar 

  12. Hasegawa, S., Kinoshita, Y., Yamada, T., Inoue, M., Bracke, S.: Disassembly reuse Part Selection for recovery rate and cost with lifetime analysis. Int. J. Autom. Technol. 12(6), 822–832 (2018)

    Article  Google Scholar 

  13. Rickli, J., Camelio, J.A.: Partial disassembly sequencing considering acquired end-of-life product age distributions. Int. J. Prod. Res. 52(7), 496–512 (2014)

    Google Scholar 

  14. Riggs, R.J., Jin, X., Hu, J.: Two-stage sequence generation for partial disassembly of products with sequence dependent task times. Procedia CIRP 29, 698–703 (2015)

    Article  Google Scholar 

  15. Jun, H.B., Cusin, M., Kiritsis, D., Xirouchakis, P.: A multi-objective evolutionary algorithm for EOL product recovery optimization: turbocharger case study. Int. J. Pro. Res. 45, 18–19 (2007)

    MATH  Google Scholar 

  16. Meng, K., Lou, P., Peng, X., Prybutok, V.: An improved co-evolutionary algorithm for green manufacturing by integration of recovery option selection and disassembly planning for end-of-life products. Int. J. Pro. Res. 54(18), 5567–5593 (2016)

    Article  Google Scholar 

  17. Geissdoerfer, M., Savaget, P., Bocken, N.M., Hultink, E.J.: The circular economy–a new sustainability paradigm? J. Clean. Prod 143, 757–768 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Elham Jelodari Mamaghani .

Editor information

Editors and Affiliations

Appendix A

Appendix A

$$\sum_{p\in P}{q}_{i}{x}_{ip1}\le {l}_{i}^{2} \quad \quad \quad \forall i\in I$$
(8)

Equations (8) confirm that a part with above a predetermined quality is not candidate to be disposed.

$$\sum_{p\in P}{q}_{i}{x}_{ip2}\ge {l}_{i}^{3} \quad \quad \quad \forall i\in I$$
(9)

Equations (9) indicate if the quality of a part is lower than a certain quality level, that part cannot be reused.

$$\sum_{p\in P}{q}_{i}{x}_{ip3}\ge {l}_{i}^{4} \quad \quad \quad \forall i\in I$$
(10)
$$\sum_{p\in P}{q}_{i}{x}_{ip3}\le 1 \quad \quad \quad \forall i\in I$$
(11)

Equations (10) and (11) define the quality of a part that has can be candidated to remanufacturing recovery operation.

$${o}_{i}=\sum_{p\in P}{q}_{i}{x}_{ip2}+1-\sum_{p\in P}{x}_{ip2} \quad \quad \quad \forall i\in I$$
(12)

Equations (12) imply to the quality of a part after reuse operation.

$$\sum_{i\in I}{o}_{i}{\alpha }_{i}\ge qm \quad \quad \quad \forall i\in I$$
(13)

Equations (13) confirm the total obtained quality should be bigger than minimum acceptable quality.

Rights and permissions

Reprints and permissions

Copyright information

© 2021 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mamaghani, E.J., Boucher, X. (2021). End-of-Life Product Recovery Optimization of Disassembled Parts Based on Collaborative Decision-Making. In: Camarinha-Matos, L.M., Boucher, X., Afsarmanesh, H. (eds) Smart and Sustainable Collaborative Networks 4.0. PRO-VE 2021. IFIP Advances in Information and Communication Technology, vol 629. Springer, Cham. https://doi.org/10.1007/978-3-030-85969-5_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-85969-5_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85968-8

  • Online ISBN: 978-3-030-85969-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics