Skip to main content

Advertisement

SpringerLink
Log in

Designing and planning a multi-echelon multi-period multi-product closed-loop supply chain utilizing genetic algorithm

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Designing and planning a closed-loop supply chain in a comprehensive structure is vital for its applicability. To cope with the design and planning issue of a comprehensive closed-loop supply chain network, this paper develops an extended model, which is multi-echelon, multi-product, and multi-period in a mixed integer linear programming framework. The word “comprehensive,” in our mathematical approach, in designing and planning a closed-loop supply chain problem, can be analyzed from two complementary angles: including all possible entities (facilities) of a real condition and considering minimum limitations on possible flows between entities. In our proposed model, customers can be supplied via manufacturers, warehouses, and distributors, as an example. The proposed model is solved by CPLEX optimization software and by a developed genetic algorithm. During this computational analysis, we compare results of proposed pretuned genetic algorithm with a global optimum of CPLEX solver. Then, a sufficient number of large-size instances are generated and solved by the proposed genetic algorithm. To the best of our knowledge, there has been no similar multi-period multi-product closed-loop supply chain design and planning problem utilizing any kind of meta-heuristics let alone genetic algorithms. Therefore, in this issue, it is an original research, and results prove the acceptable performances of the developed genetic algorithm.

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. Chopra S, Meindl P (2007) Supply chain management: strategy, planning and operation, 3rd edn. Pearson Prentice Hall Inc., New Jersey, ISBN 81-7758-003-5

    Google Scholar 

  2. Beamon BM (1999) Designing the green supply chain. Logist Inf Manag 12(4):332–342

    Article  Google Scholar 

  3. Fleischmann M, Krikke HR, Dekker R, Flapper SDP (2000) A characterisation of logistics networks for product recovery. Omega 28:653–666

    Article  Google Scholar 

  4. Schultmann F, Zumkeller M, Rentz O (2006) Modeling reverse logistic tasks within closed-loop supply chains: an example from the automotive industry. J Oper Res 171(3):1033–1050

    Article  MATH  Google Scholar 

  5. Pibernik R, Sucky E (2006) Centralised and decentralised supply chain planning. Int J Integ Supply Manag 2(1/2):6–27

    Article  Google Scholar 

  6. French ML, LaForge RL (2006) Closed-loop supply chains in process industries: an empirical study of producer re-use issues. J Oper Manag 24(3):271–286

    Article  Google Scholar 

  7. Prahinski C, Kocabasoglu C (2006) Empirical research opportunities in reverse supply chains. Omega 34(6):519–532

    Article  Google Scholar 

  8. Biehl M, Prater E, Realff MJ (2007) Assessing performance and uncertainty in developing carpet reverse logistics systems. Comput Oper Res 34(2):443–463

    Article  MATH  Google Scholar 

  9. Kusumastuti RD, Piplani R, Lim GH (2008) Redesigning closed-loop service network at a computer manufacturer: a case study. Int J Prod Econ 111(2):244–260

    Article  Google Scholar 

  10. Lee DH, Dong M (2008) A heuristic approach to logistics network design for end-of-lease computer products recovery. Transp Res E Logist Transp Rev 44(3):455–474

    Article  Google Scholar 

  11. (de la) Fuente MV, Ros L, Cardós M (2008) Integrating forward and reverse supply chains: application to a metal-mechanic company. Int J Prod Econ 111(2):782–792

    Article  Google Scholar 

  12. Amaro ACS, Barbosa-Póvoa AFPD (2008) Planning and scheduling of industrial supply chains with reverse flows: a real pharmaceutical case study. Comput Chem Eng 32(11):2606–2625

    Article  Google Scholar 

  13. Mutha A, Pokharel S (2009) Strategic network design for reverse logistics and remanufacturing using new and old product modules. Comput Ind Eng 56(1):334–346

    Article  Google Scholar 

  14. Dehghanian F, Mansour S (2009) Designing sustainable recovery network of end-of-life products using genetic algorithm. Resour Conserv Recycl 53(10):559–570

    Article  Google Scholar 

  15. Xanthopoulos A, Iakovou E (2009) On the optimal design of the disassembly and recovery processes. Waste Manage 29(5):1702–1711

    Article  Google Scholar 

  16. Kannan G, Noorul Haq A, Devika M (2009) Analysis of closed loop supply chain using genetic algorithm and particle swarm optimisation. Int J Prod Res 47(5):1175–1200

    Article  Google Scholar 

  17. Pokharel S, Mutha A (2009) Perspectives in reverse logistics: a review. Rev Article Resour Conserv Recycl 53(4):175–182

    Article  Google Scholar 

  18. Subramoniam R, Huisingh D, Chinnam RB (2009) Remanufacturing for the automotive after market- strategic factors: literature review and future research needs. J Clean Prod 17(13):1163–1174

    Article  Google Scholar 

  19. Wang HF, Hsu HW (2010) A closed-loop logistic model with a spanning tree based genetic algorithm. Comput Oper Res 37(2):376–389

    Article  Google Scholar 

  20. El-Sayed M, Afia N, El-Kharbotly A (2010) A stochastic model for forward–reverse logistics network design under risk. Comput Ind Eng 58:423–431

    Article  Google Scholar 

  21. Gold S, Seuring S, Beske P (2010) The constructs of sustainable supply chain management—a content analysis based on published case studies. Prog Ind Ecol 7(2):114–137

    Article  Google Scholar 

  22. Subramanian P, Ramkumar N, Narendran T (2010) Mathematical model for multi-echelon, multi-product, single time-period closed loop supply chain. Int J Bus Perform Supply Chain Model 2(3/4):216–236

    Article  Google Scholar 

  23. Kannan G, Sasikumar P, Devika K (2010) A genetic algorithm approach for solving a closed loop supply chain model: a case of battery recycling. Appl Math Model 34:655–670

    Article  MathSciNet  MATH  Google Scholar 

  24. Pishvaee MS, Rabbani M, Torabi SA (2011) A robust optimization approach to closed-loop supply chain network design under uncertainty. Appl Math Model 35:637–649

    Article  MathSciNet  MATH  Google Scholar 

  25. El korchi A, Millet D (2011) Designing a sustainable reverse logistics channel: the 18 generic structures framework. J Clean Prod 19(6–7):588–597

    Article  Google Scholar 

  26. Gomes Salema MI, Barbosa-Povoa AP, Novais AQ (2010) Simultaneous design and planning of supply chains with reverse flows: a generic modelling framework. Eur J Oper Res 203(2):336–349

    Article  MATH  Google Scholar 

  27. Amaro ACS, Barbosa-Póvoa AFPD (2009) The effect of uncertainty on the optimal closed-loop supply chain planning under different partnerships structure. Comput Chem Eng 33(12):2144–2158

    Article  Google Scholar 

  28. Lieckens K, Vandaele N (2007) Reverse logistics network design with stochastic lead times. Comput Oper Res 34(2):395–416

    Article  MATH  Google Scholar 

  29. Pishvaee MS, Zanjirani Farahani R, Dullaert W (2010) A memetic algorithm for bi-objective integrated forward/reverse logistics network design. Comput Oper Res 37(6):1100–1112

    Article  MATH  Google Scholar 

  30. Min H, Ko HJ (2008) The dynamic design of a reverse logistics network from the perspective of third-party logistics service providers. Int J Prod Econ 113(1):176–192

    Article  Google Scholar 

  31. Holland JH (1992) Adaptation in natural and artificial systems, 2nd edn. MIT Press, Cambridge

    Google Scholar 

  32. Hartl RE (1990) A global convergence proof for a class of genetic algorithms. Working paper, Vienna University of Technology, Institute of Biometrics, Operations Research and System Theory

  33. Radcliffe NJ (1993) Genetic set recombination. Edinburgh Parallel Computing Centre, University of Edinburgh

  34. Bean JC (1994) Genetics and random keys for sequencing and optimization. INFORMS J Comput 6(2):154–160

    Article  MATH  Google Scholar 

  35. Miller BL, Goldberg DE (1996) Genetic algorithms, tournament selection, and the effects of noise. Evol Comput 4(2):113–131

    Article  Google Scholar 

  36. Vose MD (1991) Generalizing the notion of schema in genetic algorithms. Artif Intell 50(1):385–396

    Article  MathSciNet  MATH  Google Scholar 

  37. Koza JR (1991) Genetic programming: II: automatic discovery of reusable programs. MIT Press, Cambridge

    Google Scholar 

  38. Reeves CR (1993) Modern heuristic techniques for combinatorial problems. Blackwell Scientific Publications, Oxford

    MATH  Google Scholar 

  39. Mitchell M (1996) An introduction to genetic algorithms. MIT Press, Cambridge

    Google Scholar 

  40. Baeck T, Fogel DB, Michalewicz Z (1997) Handbook of evolutionary computation. Oxford University Press/Institute of Physics Publishing, New York

    Book  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering and Management Systems, Amirkabir University of Technology (Tehran Polytechnic), 424 Hafez Ave, P.O. Box: 15875-4413, Tehran, Iran

    Hamed Soleimani, Mirmehdi Seyyed-Esfahani & Mohsen Akbarpour Shirazi

Authors
  1. Hamed Soleimani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mirmehdi Seyyed-Esfahani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohsen Akbarpour Shirazi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mirmehdi Seyyed-Esfahani.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Soleimani, H., Seyyed-Esfahani, M. & Shirazi, M.A. Designing and planning a multi-echelon multi-period multi-product closed-loop supply chain utilizing genetic algorithm. Int J Adv Manuf Technol 68, 917–931 (2013). https://doi.org/10.1007/s00170-013-4953-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-013-4953-6

Keywords

Search

Navigation