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Timing control points via simulation for production systems under random disturbances

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  • Golenko-Ginzburg, Dimitri
  • Laslo, Zohar

Abstract

The paper deals with the problem of production control with disturbances. The problem is applied to semi-automated production systems where the advancement of the process cannot be measured continuously but only in preset control (inspection) points. It is assumed that the production process can be simulated within a time interval of arbitrary size. Given the target amount needed, the due date, the routine control point, the actual accumulated production observed at that point, and the preset confidence probability (chance constraint) of meeting the deadline on time, the decision-maker has to determine the next control point. On-line control has to be carried out as rarely as possible but without missing the moment when the tendency to deviate from the planned trajectory may endanger meeting the due date on time under a chance constraint. Determining the next control point is carried out via simulation with a constant time step. At each intermediate check-point, decision-making based on sequential statistical analysis has to be undertaken, either.

Suggested Citation

  • Golenko-Ginzburg, Dimitri & Laslo, Zohar, 2001. "Timing control points via simulation for production systems under random disturbances," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 54(6), pages 451-458.
    • Handle: RePEc:eee:matcom:v:54:y:2001:i:6:p:451-458
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    References listed on IDEAS

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    1. Golenko-Ginzburg, Dmitri, 1989. "Estimating the number of control points for production systems with variable speeds," Engineering Costs and Production Economics, Elsevier, vol. 16(2), pages 97-102, April.
    2. Friedman, Lea & Golenko-Ginzburg, Dimitri & Sinuany-Stern, Zilla, 1989. "Determining control points of a production system under a chance constraint," Engineering Costs and Production Economics, Elsevier, vol. 18(2), pages 139-144, December.
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    Cited by:

    1. Narjes Sabeghi & Hamed Reza Tareghian, 2020. "Using the generalized maximum covering location model to control a project’s progress," Computational Management Science, Springer, vol. 17(1), pages 1-21, January.

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