Histogram based bounds and approximations for production lines
We present a modelling method for the analysis of production lines with generally distributed processing times and finite buffers. We consider the complete modelling process, from the data collection to the performance evaluation. First, the data about the processing times is supposed to be collected in the form of histograms. Second, tractable discrete phase-type distributions are built. Third, the evolution of the production line is described by a Markov chain, using a state model. Our originality mostly comes from the way the phase-type distributions are built: the "grouping at the end" discretization aggregates the probability mass in a time step at its end. The method allows to compute refinable upper and lower bounds on the throughput. Furthermore, we propose some approximations and show how the method performs on simple examples. We argue that the way the distributions are discretized, called "probability masses fitting", can be thought as a valuable alternative in order to build tractable distributions.
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|Date of creation:|
|Note:||In : European Journal of Operational Research, 197, 1133-1141, 2009|
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- Laoucine Kerbache & J. Macgregor Smith, 2000. "Multi-objective routing within large scale facilities using open finite queueing networks," Post-Print hal-00798811, HAL.
- Papadopoulos, H. T. & Heavey, C., 1996. "Queueing theory in manufacturing systems analysis and design: A classification of models for production and transfer lines," European Journal of Operational Research, Elsevier, vol. 92(1), pages 1-27, July.
- Kerbache, Laoucine & Smith, J. MacGregor, 2000. "Multi-objective routing within large scale facilities using open finite queueing networks," European Journal of Operational Research, Elsevier, vol. 121(1), pages 105-123, February.
- Gourgand, Michel & Grangeon, Nathalie & Norre, Sylvie, 2005. "Markovian analysis for performance evaluation and scheduling in m machine stochastic flow-shop with buffers of any capacity," European Journal of Operational Research, Elsevier, vol. 161(1), pages 126-147, February.
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