Skip to main content
SpringerLink
Log in

Bi-objective optimization of integrating configuration generation and scheduling for reconfigurable flow lines using NSGA-II

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

Abstract

Reconfigurable manufacturing system (RMS) is a new manufacturing system paradigm suited to the era of mass customization. A RMS is able to change its configuration by physical reconfiguration and/or logical reconfiguration, to provide exact functionality and capacity needed for every demand period. For the reconfigurable flow line (RFL) in which multiple parts within the same part family can be produced simultaneously, optimal design of physical configuration (called configuration generation) and proper scheduling of multiple products are crucial to operate RFL in a cost-effective manner. Considering the close coupling between configuration generation and scheduling of multiple products for RFL, a new multi-objective mixed integer programming model is established for the integrated optimization of configuration generation and scheduling. The two conflicting objectives are to minimize total cost including capital cost and reconfiguration cost and to minimize total tardiness. The validity of the model is verified through case analysis implemented by LINGO software. Subsequently, the nondominated sorting genetic algorithm II (NSGA-II) is adopted to obtain a set of nondominated solutions. In the NSGA-II, a hybrid encoding method ensuring any chromosome a feasible solution is devised. Case study is utilized to illustrate the effectiveness of the NSGA-II for solving the problem. The case study also reveals that only if performing configuration generation and scheduling concurrently can a set of solutions balancing cost and tardiness be identified effectively for the RFL.

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. Koren Y, Heisel U, Jovane F (1999) Reconfigurable manufacturing systems. Annal CIRP 48(2):527–540

    Article  Google Scholar 

  2. Mehrabi MG, Ulsoy AG, Koren Y, Heytler P (2002) Trends and perspectives in flexible and reconfigurable manufacturing systems. J Intell Manuf 13(2):135–146

    Article  Google Scholar 

  3. EIMaraghy HA (2006) Flexible and reconfigurable manufacturing systems paradigms. Int J Flex Manuf Syst 17(4):261–176

    Article  Google Scholar 

  4. Goyal KK, Jain P, Jain M (2012) Optimal configuration selection for reconfigurable manufacturing system using NSGA II and TOPSIS. Int J Prod Res 50(15):4175–4191

    Article  Google Scholar 

  5. Bi Z, Lang S, Shen W, Wang L (2008) Reconfigurable manufacturing systems: the state of the art. Int J Prod Res 46(4):967–992

    Article  MATH  Google Scholar 

  6. Ye H, Liang M (2006) Simultaneous modular product scheduling and manufacturing cell reconfiguration using a genetic algorithm. J Manuf Sci Eng 128(4):984–995

    Article  Google Scholar 

  7. Tang L (2005) Design and reconfiguration of RMS for part family. University of Michigan, Ann Arbor, MI, USA

    Google Scholar 

  8. Youssef AMA, EIMaraghy HA (2006) Modelling and optimization of multiple-aspect RMS configurations. Int J Prod Res 44(22):4929–4958

    Article  MATH  Google Scholar 

  9. Dou J, Dai X, Meng Z (2010) Optimisation for multi-part flow-line configuration of reconfigurable manufacturing system using GA. Int J Prod Res 48(14):4071–4100

    Article  MATH  Google Scholar 

  10. Dou J, Dai X, Meng Z (2011) A GA-based approach for optimizing single-part flow-line configurations of RMS. J Intell Manuf 22(2):301–317

    Article  Google Scholar 

  11. Wang H (2005) Flexible flow shop scheduling: optimum, heuristics and artificial intelligence solutions. Expert Syst 22(2):78–85

    Article  Google Scholar 

  12. Sawik T (2012) Batch versus cyclic scheduling of flexible flow shops by mixed-integer programming. Int J Prod Res 30(18):5017–5034

    Article  Google Scholar 

  13. Singh MR, Mahapatra S, Mishra K (2013) A novel swarm optimiser for flexible flow shop scheduling. Int J Swarm Intell 1(1):51–69

    Article  Google Scholar 

  14. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  15. Babu AS (2013) Reconfigurations of manufacturing systems—an empirical study on concepts, research, and applications. Int J Adv Manuf Technol 66(1–4):107–124

    Google Scholar 

  16. Renzi C, Leali F, Cavazzuti M, Andrisano AO (2014) A review on artificial intelligence applications to the optimal design of dedicated and reconfigurable manufacturing systems. Int J Adv Manuf Technol 72:403–418

    Article  Google Scholar 

  17. Son SY (2000) Design principles and methodologies for reconfigurable machining systems. University of Michigan, Ann Arbor, MI

    Google Scholar 

  18. Spicer P, Carlo HJ (2007) Integrating reconfiguration cost into the design of multi-period scalable reconfigurable manufacturing systems. J Manuf Sci Eng 129(1):202–210

    Article  Google Scholar 

  19. Dou J, Dai X, Meng Z (2009) Graph theory-based approach to optimize single-product flow-line configurations of rms. Int J Adv Manuf Technol 41(9):916–931

    Article  Google Scholar 

  20. Manira M, Pakkirisamy V, Jeyapaul R (2015) An ant colony optimization–based approach for a single-product flow-line reconfigurable manufacturing systems. Proc Inst Mech Eng B: J Eng Manuf. doi:10.1177/0954405415585260

    Google Scholar 

  21. Youssef AMA (2006) Optimal configuration selection for reconfigurable manufacturing systems. University of Windsor, Windsor, Ontario, Canada

    MATH  Google Scholar 

  22. Youssef AMA, EIMaraghy HA (2008) Availability consideration in the optimal selection of multiple-aspect RMS configurations. Int J Prod Res 46(21):5849–5882

    Article  MATH  Google Scholar 

  23. Saxena LK, Jain PK (2012) A model and optimisation approach for reconfigurable manufacturing system configuration design. Int J Prod Res 50(12):3359–3381

    Article  Google Scholar 

  24. Bryan A, Hu S, Koren Y (2013) Assembly system reconfiguration planning. J Manuf Sci Eng 135(4):1–12

    Article  Google Scholar 

  25. Hasan F, Jain P, Kumar D (2014) Optimum configuration selection in reconfigurable manufacturing system involving multiple part families. OPSEARCH 52(2):297–311

    Article  Google Scholar 

  26. Bensmaine A, Dahane M, Benyoucef L (2013) A non-dominated sorting genetic algorithm based approach for optimal machines selection in reconfigurable manufacturing environment. Comput Ind Eng 66(3):519–524

    Article  Google Scholar 

  27. Azab A, ElMaraghy H (2007) Mathematical modeling for reconfigurable process planning. CIRP Ann Manuf Technol 56(1):467–472

    Article  Google Scholar 

  28. Shabaka A, ElMaraghy H (2008) A model for generating optimal process plans in RMS. Int J Comput Integr Manuf 21(2):180–194

    Article  Google Scholar 

  29. Musharavati F, Hamouda A (2011) Modified genetic algorithms for manufacturing process planning in multiple parts manufacturing lines. Expert Syst Appl 38(9):10770–10779

    Article  Google Scholar 

  30. Musharavati F, Hamouda A (2012) Simulated annealing with auxiliary knowledge for process planning optimization in reconfigurable manufacturing. Robot Comput Integr Manuf 28(2):113–131

    Google Scholar 

  31. Bensmaine A, Dahane M, Benyoucef L (2014) A new heuristic for integrated process planning and scheduling in reconfigurable manufacturing systems. Int J Prod Res 52(12):3583–3594

    Article  Google Scholar 

  32. Chaube A, Benyoucef L, Tiwari MK (2012) An adapted NSGA-2 algorithm based dynamic process plan generation for a reconfigurable manufacturing system. J Intell Manuf 23(4):1141–1155

    Article  Google Scholar 

  33. Xie N, Li A (2006) Reconfigurable production line modeling and scheduling using petri nets and genetic algorithm. Chin J Mech Eng(English Edition) 19(3):362–367

    Article  Google Scholar 

  34. Yu JM, Doh HH, Kim JS, Kwon YJ, Lee DH, Nam SH (2013) Input sequencing and scheduling for a reconfigurable manufacturing system with a limited number of fixtures. Int J Adv Manuf Technol 67(1–4):157–169

    Article  Google Scholar 

  35. Bi Z, Lang S, Verner M, Orban P (2008) Development of reconfigurable machines. Int J Adv Manuf Technol 39(11):1227–1251

    Article  Google Scholar 

  36. Deb K (2001) Multi-objective optimization using evolutionary algorithms. John Wiley & Sons

Download references

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Southeast University, Nanjing, Jiangsu, China

    Jianping Dou & Chun Su

  2. School of Automation, Southeast University, Nanjing, Jiangsu, China

    Jun Li

Authors
  1. Jianping Dou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chun Su
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianping Dou.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dou, J., Li, J. & Su, C. Bi-objective optimization of integrating configuration generation and scheduling for reconfigurable flow lines using NSGA-II. Int J Adv Manuf Technol 86, 1945–1962 (2016). https://doi.org/10.1007/s00170-015-8291-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-015-8291-8

Keywords

Search

Navigation