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The two-machine one-buffer continuous time model with restart policy

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  • Elisa Gebennini
  • Andrea Grassi
  • Cesare Fantuzzi

Abstract

This paper deals with the performance evaluation of production lines in which well defined machine start/stop control policies are applied. A modeling approach has been developed in order to reduce the complexity of a two-machine one-buffer line where a specific control policy, called “restart policy”, is adopted. The restart policy exercises control over the start/stop condition of the first machine: when the buffer gets full and, as a consequence, the first machine is forced to stop production (i.e., it is blocked), the control policy keeps the first machine in an idle state until the buffer becomes empty again. The rationale behind this policy is to reduce the blocking frequency of the first machine, i.e. the probability that a blockage occurs on the first machine due to the buffer filling up. Such a control policy is adopted in practice when outage costs (e.g., waste production) are related to each restart of the machine. The two-machine one-buffer line with restart policy (RP line) is here modeled as a continuous time Markov process so as to consider machines having different capacities and working in an asynchronous manner. The mathematical RP model is described along with its analytical solution. Then, the most critical line performance measures are derived and, finally, some numerical examples are reported to show the effects of such a policy on the blocking frequency of the first machine. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Elisa Gebennini & Andrea Grassi & Cesare Fantuzzi, 2015. "The two-machine one-buffer continuous time model with restart policy," Annals of Operations Research, Springer, vol. 231(1), pages 33-64, August.
    • Handle: RePEc:spr:annopr:v:231:y:2015:i:1:p:33-64:10.1007/s10479-013-1373-9
    DOI: 10.1007/s10479-013-1373-9
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    References listed on IDEAS

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    1. Lutz, Christian M. & Roscoe Davis, K. & Sun, Minghe, 1998. "Determining buffer location and size in production lines using tabu search," European Journal of Operational Research, Elsevier, vol. 106(2-3), pages 301-316, April.
    2. Elisa Gebennini & Stanley Gershwin, 2013. "Modeling waste production into two-machine–one-buffer transfer lines," IISE Transactions, Taylor & Francis Journals, vol. 45(6), pages 591-604.
    3. Tan, BarIs & Gershwin, Stanley B., 2009. "Analysis of a general Markovian two-stage continuous-flow production system with a finite buffer," International Journal of Production Economics, Elsevier, vol. 120(2), pages 327-339, August.
    4. Yves Dallery & Yannick Frein, 1993. "On Decomposition Methods for Tandem Queueing Networks with Blocking," Operations Research, INFORMS, vol. 41(2), pages 386-399, April.
    5. J. MacGregor Smith & Sophia Daskalaki, 1988. "Buffer Space Allocation in Automated Assembly Lines," Operations Research, INFORMS, vol. 36(2), pages 343-358, April.
    6. Barış Tan & Stanley Gershwin, 2011. "Modelling and analysis of Markovian continuous flow systems with a finite buffer," Annals of Operations Research, Springer, vol. 182(1), pages 5-30, January.
    7. Papadopoulos, H. T. & Vidalis, M. I., 2001. "Minimizing WIP inventory in reliable production lines," International Journal of Production Economics, Elsevier, vol. 70(2), pages 185-197, March.
    8. Stanley B. Gershwin, 1987. "An Efficient Decomposition Method for the Approximate Evaluation of Tandem Queues with Finite Storage Space and Blocking," Operations Research, INFORMS, vol. 35(2), pages 291-305, April.
    9. Diomidis Spinellis & Chrissoleon Papadopoulos, 2000. "A simulated annealing approach for buffer allocation in reliable production lines," Annals of Operations Research, Springer, vol. 93(1), pages 373-384, January.
    10. Stanley Gershwin & Mitchell Burman, 2000. "A decomposition method for analyzing inhomogeneous assembly/disassembly systems," Annals of Operations Research, Springer, vol. 93(1), pages 91-115, January.
    11. Stanley Gershwin & James Schor, 2000. "Efficient algorithms for buffer space allocation," Annals of Operations Research, Springer, vol. 93(1), pages 117-144, January.
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