Skip to main content
SpringerLink
Log in

Remanufacturing closed-loop supply chain network design based on genetic particle swarm optimization algorithm

  • Published:
Journal of Central South University Aims and scope Submit manuscript

Abstract

As the huge computation and easily trapped local optimum in remanufacturing closed-loop supply chain network (RCSCN) design considered, a genetic particle swarm optimization algorithm was proposed. The total cost of closed-loop supply chain was selected as fitness function, and a unique and tidy coding mode was adopted in the proposed algorithm. Then, some mutation and crossover operators were introduced to achieve discrete optimization of RCSCN structure. The simulation results show that the proposed algorithm can gain global optimal solution with good convergent performance and rapidity. The computing speed is only 22.16 s, which is shorter than those of the other optimization algorithms.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. GUIDE V D R, HARRISON T P, WASSENHOVE L N V. The challenge of closed-loop supply chain [J]. Interfaces, 2003, 33(6): 3–6.

    Article  Google Scholar 

  2. ALTIPARMAK F, GEN M, LIN L, KORAOGLAN I. A steady-state genetic algorithm for multi-product supply chain network design [J]. Computers and Industrial Engineering, 2009, 56(2): 521–537.

    Article  Google Scholar 

  3. ALI A. Designing a distribution network in a supply chain system formulation and efficient solution procedure [J]. European Journal of Operation Research, 2006, 171(2): 567–576.

    Article  MATH  Google Scholar 

  4. QIU Ruo-zhe, HUANG Xiao-yuan. Research progress on closed-loop supply chain structure [J]. Management Review, 2007, 19(1): 49–53. (in Chinese)

    Google Scholar 

  5. DAI Ying, MA Zu-jun, LIU Fei. Optimal design of integrated logistics networks for manufacturing/remanufacturing systems based on hybrid genetic algorithm [J]. Computer Integrated Manufacturing Systems, 2006, 12(11): 1853–1859. (in Chinese)

    Google Scholar 

  6. HAMMOND D, BEULLENS P. Closed-loop supply chain network equilibriurn under legislation [J]. European Journal of Operational Research, 2007, 183(2): 895–908.

    Article  MATH  Google Scholar 

  7. SAVASKAN R C, BHATTACHARYA S, WASSENHOVE L N V. Closed-loop supply chain models with product remanufacturing [J]. Management Science, 2004, 50(2): 239–252.

    Article  MATH  Google Scholar 

  8. SAVASKAN R C, WASSENHOVE L N V. Reverse channel design: The case of competing retailers [J]. Management Science, 2006, 52(1): 1–14.

    Article  MATH  Google Scholar 

  9. GU Qiao-lun, GAO Tie-gang, SHI Lian-shuan. Price decision analysis for reverse supply chain based on game theory [J]. Systems Engineering theory and Practice, 2005, 25(3): 20–25. (in Chinese)

    Google Scholar 

  10. ZHANG Rui, ZHANG Ji-hu. Optimization of closed-loop supply chain logistics network design based on remanufacturing [J]. Journal of Qingdao University: Natural Science Edition, 2007, 20(4): 82–85. (in Chinese)

    MATH  Google Scholar 

  11. GE Shu, GAN Mi. Genetic algorithms based design of the supply chain network integrated with the reverse logistics [J]. China Railway Science, 2008, 29(6): 116–120. (in Chinese)

    Google Scholar 

  12. HUANG Hai-xin, WU Li-yong, WANG Ding-wei, XUE Shi-tong. Optimization model for a two-level distribution network and its genetic algorithm-based solution [J]. Computer Integrated Manufacturing Systems, 2004, 10(8): 914–918. (in Chinese)

    Google Scholar 

  13. SEO J H, IM C H, HEO C G, KIM J K, JUNG H K, LEE C G. Multimodal function optimization based on particle swarm optimization [J]. IEEE Transactions on Magnetic, 2006, 42(4): 1095–1098.

    Article  Google Scholar 

  14. GAO Shang, YANG Jing-yu. Swarm intelligence algorithm and its application [M]. Beijing: China Water Power Press, 2006: 86–89.

    Google Scholar 

  15. ZHOU Xian-cheng, ZHAO Zhi-xue, HE Cai-hong, XU Ge. Mixed-particle swarm optimization for two-level distribution network model [J]. Journal of Central South University: Science and Technology, 2010, 41(2): 623–627. (in Chinese)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer and Electronic Engineering, Hunan University of Commerce, Changsha, 410205, China

    Xian-cheng Zhou  (周鲜成), Zhi-xue Zhao  (赵志学) & Kai-jun Zhou  (周开军)

  2. School of Finance, Hunan University of Commerce, Changsha, 410205, China

    Cai-hong He  (贺彩虹)

Authors
  1. Xian-cheng Zhou  (周鲜成)
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhi-xue Zhao  (赵志学)
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kai-jun Zhou  (周开军)
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Cai-hong He  (贺彩虹)
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xian-cheng Zhou  (周鲜成).

Additional information

Foundation item: Project(2011ZK2030) supported by the Soft Science Research Plan of Hunan Province, China; Project(2010ZDB42) supported by the Social Science Foundation of Hunan Province, China; Projects(09A048, 11B070) supported by the Science Research Foundation of Education Bureau of Hunan Province, China; Projects(2010GK3036, 2011FJ6049) supported by the Science and Technology Plan of Hunan Province, China

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhou, Xc., Zhao, Zx., Zhou, Kj. et al. Remanufacturing closed-loop supply chain network design based on genetic particle swarm optimization algorithm. J. Cent. South Univ. Technol. 19, 482–487 (2012). https://doi.org/10.1007/s11771-012-1029-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11771-012-1029-y

Key words

Search

Navigation