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Monotonicity of Optimal Performance Measures for Polling Systems

Published online by Cambridge University Press:  27 July 2009

Mark P. Van Oyen
Affiliation:
Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208–3119

Abstract

We consider scheduling a single server in a multiclass queue subject to setup times and setup costs. We examine the issue of whether or not reductions in the mean and variance of the setup time distributions can lead to degraded system performance. Provided that setups are reduced according to a stochastically smaller ordering, we show that if an optimal policy is used both for the original system and for the system with reduced setup times, then an improvement in performance is guaranteed. Even in cases for which a truly optimal policy is unknown, idling can be employed to avoid degradation of performance as setup times are cut. We extend this approach to show that system performance is monotonic with respect to service time distributions, switching costs, holding costs, and uniform reductions in the arrival rates. Extensions to sequencedependent setups and job feedback are noted.

Type
Research Article
Copyright
Copyright © Cambridge University Press 1997

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References

1.Duenyas, I. & Van Oyen, M.P. (1996). Heuristic scheduling of parallel heterogeneous queues with set-ups. Management Science 42(6): 814829.CrossRefGoogle Scholar
2.Duenyas, I. & Van Oyen, M.P. (1996). Stochastic scheduling of parallel queues with set-ups costs. Queueing Systems (QUESTA) 22: 421444.Google Scholar
3.Federgruen, A. & Katalan, Z. (1996). The stochastic economic lot scheduling problem: Cyclical base-stock policies with idle times. Management Science 42(6): 783796.CrossRefGoogle Scholar
4.Koole, G. (1994). Assigning a single server to inhomogeneous queues with switching costs. Theoretical Computer Science (to appear).Google Scholar
5.Kumar, P.R. & Varaiya, P. (1986). Stochastic systems: Estimation, identification, and adaptive control. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
6.Levy, H. & Sidi, M. (1990). Polling systems: Applications, modeling, and optimization. IEEE Transactions on Communication 38: 17501760.CrossRefGoogle Scholar
7.Liu, Z., Nain, P., & Towsley, D. (1992). On optimal polling policies. Queueing Systems (QUESTA) 11: 5983.CrossRefGoogle Scholar
8.Righter, R. & Shanthikumar, J.G. (1994). Multi-class production systems with setup time. Preprint.Google Scholar
9.Ross, S. (1983). Introduction to stochastic dynamic programming. New York: Academic Press.Google Scholar
10.Ross, S.M. (1983). Stochastic processes. New York: Wiley.Google Scholar
11.Sarkar, D. & Zangwill, W.I. (1991). Variance effects in cyclic production systems. Management Science 37: 444453.CrossRefGoogle Scholar
12.Takagi, H. (1994). Queueing analysis of polling models: Progress in 1990–1993. In Dshalalow, J.H. (ed.), Frontiers in queueing: Models, methods and problems. Boca Raton, FL: CRC Press.Google Scholar
13.Zangwill, W.I. (1992). The limits of Japanese production theory. Interfaces 22: 1425.CrossRefGoogle Scholar
14.Zangwill, W.I. & Sarkar, D. (1994). Response to comments on our work by Duenyas, by Gerchak and Zhang, and by Mclntyre. Interfaces 24: 9094.Google Scholar