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Generic Routings for ConWip Sizing in a Multi-product Environment

  • Yann Jaegler
  • Samir Lamouri
  • Damien Trentesaux
  • Patrick Burlat
  • Anicia Jaegler
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 762)

Abstract

In a vulnerable, uncertain, complex, and ambiguous environment, ConWip provides a sustainable, effective and adaptive production control system for manufacturers. The present paper deals with the key questions related to the implementation of ConWip in a high product mix and/or high routing mix environment. To respond to this challenge, generic routing has to be defined to cover all of the routings of the high product mix. Through four algorithms, this paper studies how to define a representative routing. A numerical sample is derived from industrial data. We implement it in the four algorithms to generate four different generic routings. Then, thanks to Wipsim, an engineering tool used in projects to design and improve ConWip lines, we calculate the optimized ConWip parameters for each of them. Finally, we compare the results and highlight some research avenues.

Keywords

ConWip Production control system Routing Complex environment Management of production Customer-oriented logistics Cyber-physical manufacturing systems Industry 4.0 

References

  1. 1.
    Little, J.D.: A proof for the queuing formula: L = λ W. Oper. Res. 9, 383–387 (1961)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Jaegler, Y., Jaegler, A., Trentesaux, D., Burlat, P., Lamouri, S.: The CONWIP production control system: classification and discussion of current and future research. J. Eur. Systèmes Autom. 1–22 (2017)Google Scholar
  3. 3.
    Spearman, M.L., Zazanis, M.A.: Push and pull production systems: issues and comparisons. Oper. Res. 40, 521–532 (1992)CrossRefzbMATHGoogle Scholar
  4. 4.
    Germs, R., Riezebos, J.: Workload balancing capability of pull systems in MTO production. Int. J. Prod. Res. 48, 2345–2360 (2010)CrossRefzbMATHGoogle Scholar
  5. 5.
    Ziengs, N., Riezebos, J., Germs, R.: Placement of effective work-in-progress limits in route-specific unit-based pull systems. Int. J. Prod. Res. 50, 4358–4371 (2012)CrossRefGoogle Scholar
  6. 6.
    Khojasteh-Ghamari, Y.: A performance comparison between kanban and CONWIP controlled assembly systems. J. Intell. Manuf. 20, 751–760 (2009)CrossRefGoogle Scholar
  7. 7.
    Parvin, H., Van Oyen, M.P., Pandelis, D.G., Williams, D.P., Lee, J.: Fixed task zone chaining: worker coordination and zone design for inexpensive cross-training in serial CONWIP lines. IIE Trans. 44, 894–914 (2012)CrossRefGoogle Scholar
  8. 8.
    Satyam, K., Krishnamurthy, A.: Performance analysis of CONWIP systems with batch size constraints. Ann. Oper. Res. 209, 85–114 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Eng, C.K., Sin, L.K.: CONWIP based control of a semiconductor end of line assembly. Procedia Eng. 53, 607–615 (2013)CrossRefGoogle Scholar
  10. 10.
    Prakash, J., Chin, J.F.: Modified CONWIP systems: a review and classification. Prod. Plan. Control. 26, 296–307 (2015)Google Scholar
  11. 11.
    Bahaji, N., Kuhl, M.: A simulation study of new multi-objective composite dispatching rules, CONWIP, in semiconductor fabrication. Int. J. Prod. Res. 46, 3801–3824 (2008)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Yann Jaegler
    • 1
  • Samir Lamouri
    • 1
  • Damien Trentesaux
    • 2
  • Patrick Burlat
    • 3
  • Anicia Jaegler
    • 4
  1. 1.Arts et Métiers ParisTechLAMIH UMR CNRS 8201ParisFrance
  2. 2.LAMIH UMR CNRS 8201, Université de Valenciennes et du Hainaut-CambrésisValenciennesFrance
  3. 3.Société Wipsim sasSt ÉtienneFrance
  4. 4.Center of Excellence in Supply ChainKedge Business SchoolBordeauxFrance

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