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On using continuous flow lines to model discrete production lines

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Abstract

We extend previous research on the use of models ofcontinuous tandem (CT) lines for performance analysis ofdiscrete tandem (DT) production lines. We formalize the translation of input parameters from the DT line to the CT model, as well as the translation of performance measures (PMs) obtained from the CT model back to the DT line. We show that although the CT model conceptually represents a line with continuous fluid, it can be represented as a generalized semi-Markov process (GSMP). This representation leads to a simple and concise simulation algorithm for a CT model. We investigate the accuracy of the CT model for prediction of PMs in the DT line, and show that, with proper translation of parameters and PMs, the CT model provides reasonable estimates for the DT line PMs. We provide preliminary results on gradient estimation for CT models via infinitestimal perturbation analysis. The aim of the paper is to provide a firm foundation for the future exploration of CT models as a means to parameter optimization for DT lines.

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References

  • R. Akella and P. R. Kumar. Optimal control of production rate in a failure prone manufacturing system.IEEE Trans. Autom. Control, 31(2):116–126, 1986.

    Google Scholar 

  • T. Altiok and S. Stidham, Jr.: A note on transfer lines with unreliable machines, random processing times, and finite buffers.IIE Trans. 14(2):125–127, 1982.

    Google Scholar 

  • R. Alvarez, Y. Dallery, and R. David. An experimental study of the continuous flow model of transfer lines with unreliable machines and finite buffers. InIMACS-IFAC Symposium, Modelling and Control of Technological Systems, Lille, France, 1991.

  • J. Banks and J. S. Carson, II.Discrete-Event System Simulation. Prentice-Hall, 1984.

  • J. A. Buzacott. Automatic transfer lines with buffer stocks.Int. J. Prod. Res., 5(3):183–200, 1967.

    Google Scholar 

  • J. A. Buzacott and L. E. Hanifin. Models of automatic transfer lines with inventory banks: A review and comparison.AIIE Trans., 10(2):197–207, 1978.

    Google Scholar 

  • X. R. Cao and Y. C. Ho. Sensitivity analysis and optimization of throughput in a production line with blocking.IEEE Trans. Autom. Control., AC-32(11):959–967, 1987.

    Google Scholar 

  • X. R. Cao and Y. C. Ho. Models of discrete event dynamic systems.IEEE Control Systems Magazine, 10(4):69–76, 1990.

    Google Scholar 

  • M. Caramanis. Production system design: A discrete event dynamic system and generalized Benders' decomposition approach.Int. J. Prod. Res. 25(8):1223–1234, 1987.

    Google Scholar 

  • Y. F. Choong and S. B. Gershwin. A decomposition method for the approximate evaluation of capacitated transfer lines with unreliable machines and random processing times.IIE Trans., 19(2):150–159, 1987.

    Google Scholar 

  • Y. Dallery, R. David, and X.-L. Xie. An efficient algorithm for analysis of transfer lines with unreliable machines and finite buffers.IIE Trans., 20(3):280–283, 1988.

    Google Scholar 

  • Y. Dallery, R. David, and X.-L. Xie. Approximate analysis of transfer lines with unreliable machines and finite buffers.IEEE Trans. Autom. Control., 34(9):943–953, 1989.

    Google Scholar 

  • Y. Dallery and S. B. Gershwin. Manufacturing flow line systems: A review of models and analytical results. InQueueing Systems: Theory and Applications, Special Issue on Queueing Models of Manufacturing Systems, Vol. 12, pp. 3–94, 1992.

  • H. D'Angelo, M. Caramanis, S. Finger, A. Mavretic, Y. A. Phillis, and E. Ramsden. Event-driven model of an unreliable production line with storage. InProc. 24th IEEE Conf. on Decision and Control, Ft. Lauderdale, FL, December, pp. 1694–1698, 1985.

  • H. D'Angelo, M. Caramanis, S. Finger, Y. A. Phillis, and E. Ramsden. Event-driven model of unreliable production lines with storage.Int. J. Prod. Res., 26(7):1173–1182, 1988.

    Google Scholar 

  • R. David, X. L. Xie, and Y. Dallery. Properties of continuous models of transfer lines with unreliable machines and finite buffers.IMA J. of Math. Applied in Business and Industry, 6:281–308, 1990.

    Google Scholar 

  • R. De Koster and J. Wijngaard. Continuous vs. discrete models for production lines with blocking. In H. G. Perros and T. Altiok, editors,Queueing Networks with Blocking. North-Holland, 1989.

  • D. Dubois and J. P. Forestier. Productivité et en-cours moyens d'un ensemble de deux machines séparées par une zône de stockage.RAIRO Automatique, 16(2):105–132, 1982.

    Google Scholar 

  • M. C. Fu and J.-Q. Hu. Extensions and generalizations of smoothed perturbation analysis in a generalized semi-Markov process tramework.IEEE Trans. Autom. Control., AC-37(2):1483–1500, 1992.

    Google Scholar 

  • S. B. Gershwin. An efficient decomposition method for the approximate evaluation of tandem queues with finite storage and blocking.Oper. Res., 35(2):291–305, 1987.

    Google Scholar 

  • S. B. Gershwin. An efficient decomposition algorithm for unreliable tandem queueing systems with finite buffers. In H. G. Perros and T. Altiok, editors,Queueing Networks with Blocking. North-Holland, 1989.

  • S. B. Gershwin and O. Berman. Analysis of transfer lines consisting of two unreliable machines with random processing times and finite storage buffers.AIIE Trans., 13(1):2–11, 1981.

    Google Scholar 

  • S. B. Gershwin and I. C. Schick. Continuous model of an unreliable two-stage material flow system with a finite interstage buffer. MIT Technical Report LIDS-R-1039, 1980.

  • S. B. Gershwin and I. C. Schick. Modeling and analysis of three-stage transfer lines with unreliable machines and finite buffers.Oper. Res., 31(2):354–380, 1983.

    Google Scholar 

  • P. Glasserman.Gradient Estimation Via Perturbation Analysis. Kluwer Academic Publishers, 1991.

  • P. W. Glynn. Optimization of stochastic systems. InProc. 1986 Winter Simulation Conf.., pp. 52–59, 1986.

  • P. W. Glynn. Likelihood ratio gradient estimation: An overview.Proc. 1987 Winter Simulation Conf., pp. 366–374, 1987.

  • P. W. Glynn. A GSMP formalism for discrete event systems.Proc. IEEE., 77(1):14–23, 1989.

    Google Scholar 

  • P. W. Glynn. Pathwise convexity and its relation to convergence of time-average derivatives.Management Sci., 38(9):1360–1366, 1992

    Google Scholar 

  • P. W. Glynn and W. Whitt. Indirect estimation viaLW Oper. Res., 37(1):82–103, 1989.

    Google Scholar 

  • Y. C. Ho. Performance evaluation and perturbation analysis of discrete event dynamics systems.IEEE Trans. Autom. Control., AC-32(7):563–572, 1987.

    Google Scholar 

  • Y. C. Ho, M. A. Eyler, and T. T. Chien. A gradient technique for general buffer storage design in a production line.Int. J. Prod. Res., 17(6):557–580, 1979.

    Google Scholar 

  • Y. C. Ho, M. A. Eyler, and T. T. Chien. A new approach to determine parameter sensitivities of transfer lines.Management Sci., 29(6):700–714, 1983.

    Google Scholar 

  • J.-Q. Hu. Convexity of sample path performance and strong consistency of infinitesimal perturbation analysis estimates.IEEE Trans. Autom. Control., 37(2):258–262, 1992.

    Google Scholar 

  • V. S. Kouikoglou and Y. A. Phillis. An exact effccient discrete-event model for production lines with buffers.Proc. 28th IEEE Conf. on Decision and Control, Tampa, FL, pp. 65–70, December 1989.

  • V. S. Kouikoglou and Y. A. Phillis. An efficient discrete-event model for production networks of general geometry.Proc. 29th IEEE Conf. on Decision and Control, Honolulu, HI, pp. 3446–3451, December 1990.

  • V. S. Kouikoglou and Y. A. Phillis. An exact discrete-event model and control policies for production lines with buffers.IEEE Trans. Autom. Control., 36(5):515–527, 1991.

    Google Scholar 

  • V. S. Kouikoglou and Y. A. Phillis. A serial finite unreliable queue model for production lines.Proc. 31st IEEE Conf. on Decision and Control, Tucson, AZ, pp. 1659–1664, December 1992.

  • A. M. Law and W. D. Kelton.Simulation Modeling and Analysis, McGraw-Hill, 1982.

  • P. L'Ecuyer and G. Perron. On the convergence rates of IPA and FDC derivative estimators. Submitted for publication, 1992.

  • R. Malhamé and E. K. Boukas. Transient and steady-states of the statistical flow balance equations in manufacturing sysems. InProc. Third ORSA/TIMS Conf. on Flexible Manufacturing Systems: Operations Research Models and Applications. K. E. Stecke and R. Suri eds. Elsevier, 339–345, 1989.

  • D. Mitra. Stochastic theory of a fluid model of producers and consumers coupled by a buffer.Adv. Appl. Prob., 20:646–676, 1988.

    Google Scholar 

  • C. D. Pegden, R. E. Shannon, and R. D. Sadowski.Introduction to Simulation Using SIMAN. McGraw-Hill, 1990.

  • Y. A. Phillis and V. S. Kouikoglou. Techniques in modeling and control policies for production networks. In C. T. Leondes,Control and Dynamic Systems, Vol. 47, Part 3, pp. 1–50, 1991.

  • E. Ramsden, M. Ruane, A. Mavretic, M. Caramanis, and H. D'Angelo. The use of hardware simulators in modeling production networks.Large Scale Systems, Vol. 11, pp. 149–164, 1986.

    Google Scholar 

  • S. M. Robinson. Convergence of subdifferentials under strong stochastic convexity. Submitted toManagement Science, 1993.

  • B. A. Sevast'yanov. Influence of storage bin capacity on the average standstill time of a production line.Theory of Probability and Its Applications, 7(4):429–438, 1962.

    Google Scholar 

  • A. Sharifnia. Production control of a manufacturing system with multiple machine states.IEEE Trans. Autom. Control., 33(7):620–625, 1988.

    Google Scholar 

  • A. Sharifnia, M. Caramanis, and S. B. Gershwin. Dynamic set-up scheduling and flow control in flexible manufacturing systems. In K. E. Stecke and R. Suri, editors,Proc. Third ORSA/TIMS Conference on Flexible Manufacturing Systems: Operations Research Models and Applications, pp. 327–332. Elsevier, 1989.

  • R. Suri. Infinitesimal perturbation analysis for general discrete event systems.J. ACM, 34(3):686–717, 1987.

    Google Scholar 

  • R. Suri. Perturbation analysis: The state of the art and research issues explained via the GI/G/1 queueProc. IEEE, 77(1):114–137, 1989.

    Google Scholar 

  • R. Suri and G. W. Diehl. A variable buffer-size model and its use in analyzing closed queueing networks with blocking.Management Sci., 32(2):206–224.

  • R. Suri and B.-R. Fu. Flow rate gradient estimation for continuous flow production lines. University of Wisconsin-Madison. Working paper, 1992.

  • R. Suri and B.-R. Fu. Using continuous flow models to enable rapid analysis and optimization of discrete production lines—A progress report. InProc. 19th Annual NSF Grantees Conf. on Design and Manufacturing Systems Research, pp. 1229–1238, 1993.

  • R. Suri and Y. T. Leung. Single run optimization of a SIMAN model for closed loop flexible assembly systems. InProc. 1987 Winter Simulation Conf., pp. 738–748, 1987.

  • R. Suri and Y. T. Leung. Single run optimization of discrete event simulations—An empirical study using the M/M/I queue.IEE Trans., 21(1):35–49, 1989.

    Google Scholar 

  • R. Suri, J. Sanders, and M. Kamath. Performance evaluation of production networks. In S. C. Graves, A. H. G. Rinnooy Kan, and P. H. Zipkin, editors,Handbooks in Operations Research, Vol. 4. Elsevier, 1993.

  • K. C. Wei, Q. Q. Tsao, and N. C. Otto. Determining buffer size requirements using stochastic approximation methods. Technical Report SR-89-73. Ford Research, 1989.

  • W. Whitt. Continuity of generalized semi-Markov processes.Math. of Oper. Res., 5(4):494–501, 1980.

    Google Scholar 

  • J. Wijngaard. The effect of interstage buffer storage on the output of two unreliable production units in series, with different production rates.AIIE Trans., 11(1):42–47, 1979.

    Google Scholar 

  • S. Yeralan, W. E. Franck, Jr., and M. A. Quasem. A continuous materials flow production line model with station breakdown.European J. of Operational Res., 27:289–300, 1986.

    Google Scholar 

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Suri, R., Fu, BR. On using continuous flow lines to model discrete production lines. Discrete Event Dyn Syst 4, 129–169 (1994). https://doi.org/10.1007/BF01441209

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