Buradasınız

Modeling, Simulation and Performance Analysis of a Flexible Production System

Journal Name:

Publication Year:

Abstract (2. Language): 
This paper deals with a flexible production system modeled by re-entrant queueing network; a system decomposed into two fundamental multi-productive stations and three classes, a part follows the route fixed by the system, where each one is processed first by station 1 for the first step, then by station 2 for the second step, and again by the first station for third and last step before leaving the system. We assume that there is an infinite supply of work available, so that there are always parts ready for processing step 1, and that the first station gives preemptive priority to buffer 3. Several performance measures have been used to evaluate the system performances considering two scenarios; high priority with service conservation and high priority with loss of parts. So, performances due to varying its parameters are investigated through expanded Monte Carlo simulations.
26
49

REFERENCES

References: 

[1] I. Adan and G. Weiss. A two node Jackson network with infinite supply of work, Proba-
bility in the Engineering and Informational Sciences 19 (2), 191–212. 2005.
[2] I. Adan and G. Weiss. Analysis of a simple Markovian re-entrant line with infinite supply
of work under the LBFS policy, Queueing Systems 54 (3), 169–183. 2006.
[3] F. Baccelli and S. Foss. Ergodicity of Jackson-type queueing networks. Queueing Systems
17 (1), 5–72. 1994.
[4] M. Bramson. Stability of Queueing Networks, (Springer, Berlin). 2008.
[5] M. Bruccoleri, N. L. Sergio, and G. Perrone. An object-oriented approach for flexible man-
ufacturing controls systems analysis and design using the unified modeling language, (International
Journal of Flexible Manufacturing Systems). 15 (3), 195–216. 2003.
[6] P. J. Burke. The output of a queueing system, Operations Research 4 699–704. 1956.
[7] H. Chen and A. Mandelbaum. Discrete flow networks: bottleneck analysis and fluid approximations,
Mathematics of Operations Research 16 (2), 408–446. 1991.
[8] H. Chen and A. Mandelbaum. Stochastic discrete flow networks: diffusion approximations
and bottlenecks, The Annals of Probability 19(4), 1463–1519. 1991.
[9] H. Chen and H. Zhang. Stability of multiclass queueing network under priority service
disciplines. Operations Research. 48 (1), 26–37. 2000.
[10] H. Chen and D. D. Yao. Fundamentals of Queueing Networks: Performance, Asymptotics,
and Optimization. Springer, New York. 2001.
[11] J. G. Dai, On positive Harris recurrence of multiclass queueing networks: a unified approach
via fluid limit models, The Annals of Applied Probability 5 (1), 49–77. 1995.
[12] J. G. Dai, and H. V. V. Vate. The stability of two-station multi-type fluid networks, Opera-
tions Research 48 (5), 721–744. 2000.
[13] J. G. Dai and G. Weiss. Stability and instability of fluid models for re-entrant lines, Math-
ematics of Operations Research 21, 115–134. 1996.
[14] G. M. Delgadillo and S. B. Llano. Scheduling application using petri nets: a case study:
intergra’ficas, (s.a. In: Proceedings of 19th international conference on production research,
Valparaiso, Chile). 2006.
[15] S. G. Foss. Ergodicity of queueing networks, Siberian Mathematical Journal 32 (4), 184–
203. 1991.
[16] Y. Guo. Fluid model criterion for instability of re-entrant line with infinite supply of work,
TOP 17 (2009) 305–319.
REFERENCES 49
[17] Y. Guo and H. Zhang. On the stability of a simple re-entrant line with infinite supply,
Operations Research Transactions 10 (2) 75–85. 2006.
[18] J. M. Harrison. Brownian models of queueing networks with heterogeneous customer
populations, In: Fleming, W., Lions, P. L. (Eds.) Stochastic Differential Systems, Stochastic
Control Theory and Applications, Springer, New York 147–186. 1988.
[19] Y. M. Huang, J. N. Chen, T. C. Huang, Y. L. Jeng, and Y. H. Kuo. Standardized course
generation process using dynamic fuzzy petri nets, Expert Systems with Applications 34,
72–86. 2008.
[20] P. R. Kumar. Re-entrant lines, Queueing Systems 13 (1), 87–110. 1993.
[21] S. Kumar and P.R. Kumar. Performance Bounds for Queueing Networks and Scheduling
Policies, IEEE Transactions on Automatic Control 38, 1600–1611. 1994.
[22] R. Liu, A. Kumar, and W. van der Aalst. A formal modelling approach for supply chain
event management, Decision Support Systems 43, 761-778. 2007.
[23] S. H. Lu and P. R. Kumar. Distributed scheduling based on due dates and buffer priorities,
IEEE Transactions on Automatic Control 36, 1406–1416. 1991.
[24] S. P. Meyn. Control Techniques for Complex Networks, (Cambridge University Press, Cambridge).
2008.
[25] S. P. Meyn and D. Down. Stability of generalized Jackson networks, The Annals of Applied
Probability 4 (1), 124–148. 1994.
[26] Y. Nazarathy. On control of queueing networks and the asymptotic variance rate of outputs,
(Ph.D. thesis, The University of Haifa). 2008.
[27] Y. Nazarathy and G.Weiss. Near optimal control of queueing networks over a finite time
horizon, Annals of Operations Research., 170 (1), 233–249. 2008.
[28] A. N. Rybko and A. L. Stolyar. Ergodicity of stochastic processes describing the operation
of open queueing networks, Problems of Information Transmission 28(3), 3–26. 1992.
[29] B. Shnits, J. Rubinovittz, and D. Sinreich. Multi-criteria dynamic scheduling methodology
for controlling a flexible manufacturing system, International Journal of Production
Research 42, 3457–3472. 2004.
[30] F. Tüysüz and C. Kahraman. Modeling a flexible manufacturing cell using stochastic Petri
nets with fuzzy parameters. (Expert Systems with Applications), 5(37). 2009.
[31] G. Weiss. Stability of a simple re-entrant line with infinite supply of work - the case of
exponential processing times, Journal of the Operations Research Society of Japan. 47,
304–313. 2004.
[32] G. Weiss. Jackson networks with unlimited supply of work, Journal of Applied Probability
42 (3), 879–882. 2005.

Thank you for copying data from http://www.arastirmax.com