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Optimal time to repair a broken server

Published online by Cambridge University Press:  01 July 2016

Awi Federgruen*
Affiliation:
Columbia University
Kut C. So*
Affiliation:
AT&T Bell Laboratories
*
Postal address: Graduate School of Business. 412 Uris Hall, Columbia University, New York, NY 10027, USA. Research partly supported by NSF Grant no. ECS-8604409, and partly carried out at the Hebrew University, Jerusalem.
∗∗Postal address: AT&T, Bell Laboratories, P.O. Box 900, Princeton, NJ 08540, USA.

Abstract

We consider a single-server queueing system with Poisson arrivals and general service times. While the server is up, it is subject to breakdowns according to a Poisson process. When the server breaks down, we may either repair the server immediately or postpone the repair until some future point in time. The operating costs of the system include customer holding costs, repair costs and running costs. The objective is to find a corrective maintenance policy which minimizes the long-run average operating costs of the system. The problem is formulated as a semi-Markov decision process. Under some mild conditions on the repair time and service time distributions and the customer holding cost rate function, we prove that there exists an optimal stationary policy which is characterized by a single threshold parameter: a repair is initiated if and only if the number of customers in the system exceeds this threshold. We also show how the average cost under such policies may be computed and how an optimal policy may efficiently be determined.

Type
Research Article
Copyright
Copyright © Applied Probability Trust 1989 

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References

[1] Balachandran, K. R. (1973) Control policies for a single server system. Management Sci. 19, 10131018.Google Scholar
[2] Balachandran, K. R. and Tijms, H. C. (1975) On the D-policy for the M/D/1 queue. Management Sci. 21, 10731076.Google Scholar
[3] Bell, C. (1971) Characterization and computation of optimal policies for operating an M/G/1 queueing system with removable server. Operat. Res. 19, 208218.CrossRefGoogle Scholar
[4] Bertsekas, D. P. (1976) Dynamic Programming and Stochastic Control. Academic Press, New York.Google Scholar
[5] Federgruen, A. and Green, L. (1986) Queueing systems with service interruptions. Operat. Res. 34, 752768.CrossRefGoogle Scholar
[6] Federgruen, A. and Green, L. (1988) Queueing systems with service interruptions II. Naval Res. Logist. Quart. 35, 345358.Google Scholar
[7] Federgruen, A., Hordijk, A., and Tijms, H. C. (1979) Denumberable state semi-Markov decision processes with unbounded costs, average cost criterion. Stoch. Proc. Appl. 9, 222235.Google Scholar
[8] Federguen, A. and So, K. C. (1989) Optimal maintenance policies for single-server queueing systems subject to breakdowns. Operat. Res. To appear.Google Scholar
[9] Gaver, D. P. Jr. (1962) A waiting line with interrupted service, including priorities. J. R. Statist. Soc. B24, 7390.Google Scholar
[10] Heyman, D. (1977) The T-policy of the M/G/l queue. Management Sci. 23, 775778.CrossRefGoogle Scholar
[11] Mccall, J. J. (1965) Maintenance policies for stochastically failing equipment: a survey. Management Sci. 11, 493624.Google Scholar
[12] Neuts, M. F. (1981a) Matrix Geometric Solutions in Stochastic Models. Johns Hopkins University Press, Baltimore, Maryland.Google Scholar
[13] Neuts, M. F. (1981b) Stationary waiting time distributions in the GI/PH/1 queue. J. Appl. Prob. 18, 901912.Google Scholar
[14] Pierskalla, W. P. and Voelker, J. A. (1976) A survey of maintenance models: the control and surveillance of deteriorating systems. Naval Res. Logist. Quart. 23, 353388.Google Scholar
[15] Ross, S. M. (1970) Applied Probability Models with Optimization Applications. Holden-Day, San Francisco.Google Scholar
[16] Schweitzer, P. J. (1971) Iterative solution of the functional equations for undiscounted Markov renewal programming. J. Math. Anal. Appl. 34, 495501.CrossRefGoogle Scholar
[17] Selby, S. M. (1980) Standard Mathematical Tables. CRC Press, Cleveland.Google Scholar
[18] Sherif, Y. S. and Smith, M. L. (1981) Optimal maintenance models for systems subject to failure—a review. Naval Res. Logist. Quart. 28, 4754.Google Scholar
[19] So, K. C. (1985) Optimal Maintenance Policies for Single-Server Queueing Systems Subject to Breakdowns. Unpublished Ph.D. dissertation. Stanford University.Google Scholar
[20] Sobel, M. F. (1969) Optimal average cost policy for a queue with start-up and shut-down costs. Operat. Res. 17, 145162.Google Scholar
[21] Tijms, H. C. (1986) Stochastic Modelling and Analysis: A Computational Approach. Wiley, Chichester.Google Scholar
[22] Weber, R. R. and Stidham, S. Jr. (1987) Optimal control of service rates in networks of queues. Adv. Appl. Prob. 19, 202218.Google Scholar
[23] White, H. and Christie, L. S. (1958) Queueing with preemptive priorities or with breakdowns. Operat. Res. 6, 7995.CrossRefGoogle Scholar
[24] Wolff, R. W. (1984) Conditions for finite ladder height and delay moments. Operat. Res. 32, 909916.CrossRefGoogle Scholar
[25] Yadin, M. and Naor, P. (1963) Queueing systems with a removable service station. Operat. Res. Quart. 14, 393405.Google Scholar