Advertisement

A Control Theoretic Evaluation of Schedule Nervousness Suppression Techniques for Master Production Scheduling

  • Martin W. Braun
  • Jay D. Schwartz
Chapter

Abstract

In manufacturing operations, a Master Production Schedule (MPS) can be used to make mid-range planning decisions that not only influence the production decisions for a manufacturing facility, but serve as input into other decision systems to determine materials ordering, staffing, and other business requirements. With the advance of computing and data acquisition technologies, an MPS can be recomputed on a more frequent basis to make the production schedule more agile in meeting customer needs. However, uncertainty in the demand forecast or production model may also increase the possibility and/or severity of “schedule nervousness”. The mitigation techniques of frozen horizon, move suppression, and schedule change suppression are evaluated to determine the robust stability margins of each approach at their performance-optimal tunings. Since an MPS is typically computed using Linear Programming these techniques are formulated in this manner, and therefore an empirical Nyquist stability analysis using Empirical Transfer Function Estimates (ETFE) is employed. The technique of move suppression is shown to provide better robust stability margins in the small-scale problem. Further evaluation is needed on scheduling problems of industrial size.

Keywords

Inventory Level Throughput Time Schedule Change Move Suppression Nyquist Curve 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Stadtler H (2005) Supply chain management and advanced planning—basics overview and challenges. Eur J Oper Res 163:575–588CrossRefGoogle Scholar
  2. 2.
    Law KMY, Gunasekaran A (2009) A comparative study of schedule nervousness among high-tech manufacturers across the Straits. Int J Prod Res 20:31–39Google Scholar
  3. 3.
    Sridharan V, Berry WL, Udayabhanu V (1987) Freezing the master production schedule under rolling planning horizons. Manag Sci 33(9):1137–1149CrossRefGoogle Scholar
  4. 4.
    Zhao X, Lee TS (1993) Freezing the master production schedule for materials requirements planning systems under demand uncertainty. J Oper Manag 11:185–205CrossRefGoogle Scholar
  5. 5.
    Tang O, Grubbström RW (2002) Planning and replanning the master production schedule under demand uncertainty. Int J Prod Econ 78:323–334CrossRefGoogle Scholar
  6. 6.
    Saffer DR, Doyle FJ (2004) Analysis of linear programming in model predictive control. Comput Chem Eng 28:2749–2763CrossRefGoogle Scholar
  7. 7.
    Campo PJ, Morari M (1986) \(\infty\)-Norm formulation of model predictive control problems. American control conference, June 1986, pp 339–343Google Scholar
  8. 8.
    Pujawan IN (2004) Schedule nervousness in a manufacturing system: A case study. Prod Planning In: Control 15(5):515–524Google Scholar
  9. 9.
    Sahin F, Robinson EP, Gao L (2008) Master production scheduling policy and rolling schedules in a two-stage make-to-order supply chain. Int J Prod Econ 115:528–541CrossRefGoogle Scholar
  10. 10.
    Ljung L (1999) System identification: theory for the user, 2nd edn. Prentice-Hall, Upper Saddle RiverGoogle Scholar
  11. 11.
    Blackman RW, Tukey JW (1958) The measurement of power spectra. Dover Publications, New YorkGoogle Scholar
  12. 12.
    Dolgui A, Prodhon C (2007) Supply planning under uncertainties in MRP environments: A state of the art. Ann Rev Control 31:269–279CrossRefGoogle Scholar
  13. 13.
    Haugen F (2005) Discrete-time signals and systems. Tutorial, http://techteach.no/adm/fh/
  14. 14.
    Plummer AR, Ling CS (2000) Stability and robustness of discrete-time systems with control signal saturation. Proc Inst Mech Eng part I: J Syst Control Eng 214(1):65–76Google Scholar

Copyright information

© Springer-Verlag London 2012

Authors and Affiliations

  1. 1.Intel CorporationChandlerUSA

Personalised recommendations