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Control of arrivals to a stochastic input–output system

Published online by Cambridge University Press:  01 July 2016

Søren Glud Johansen*
Affiliation:
University of Aarhus
Shaler Stidham Jr*
Affiliation:
North Carolina State University at Raleigh
*
Postal address: Department of Operations Research, University of Aarhus, Building 530, Ny Munkegade, 8000 Aarhus C, Denmark.
∗∗Postal address: Department of Industrial Engineering, North Carolina State University, Box 5111, Raleigh, NC 27650, U.S.A.

Abstract

The problem of controlling input to a stochastic input-output system by accepting or rejecting arriving customers is analyzed as a semi-Markov decision process. Included as special cases are a GI/G/1 model and models with compound input and/or output processes, as well as several previously studied queueing-control models. We establish monotonicity of socially and individually optimal acceptance policies and the more restrictive nature of the former, with random rewards for acceptance and both customer-oriented and system-oriented non-linear waiting costs. Distinctive features of our analysis are (i) that it allows dependent interarrival times and (ii) that the monotonicity proofs do not rely on the standard concavity-preservation arguments.

Type
Research Article
Copyright
Copyright © Applied Probability Trust 1980 

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Footnotes

Research partially supported by NATO Research Grant No. SRG. SS. 5, administered by the NATO Special Programme Panel on System Science.

References

[1] Adler, I. and Naor, P. (1969) Social optimization versus self-optimization in waiting lines. Tech. Rpt. No. 4, Department of Operations Research, Stanford University.Google Scholar
[2] Boxma, O. J. (1975) The single-server queue with random service output. J. Appl. Prob. 12, 763778.CrossRefGoogle Scholar
[3] Cramer, M. (1971) Optimal customer selection in exponential queues. ORC 71-24, Operations Research Center, University of California, Berkeley.Google Scholar
[4] Doshi, B. T. (1977) Continuous-time control of the arrival process in an M/G/l queue. Stoch. Proc. Appl. 5, 265284.Google Scholar
[5] Hillier, F. S. (1963) Economic models for industrial waiting-line problems. Management Sci. 10, 119130.CrossRefGoogle Scholar
[6] Knudsen, N. C. (1972) Individual and social optimization in a multi-server queue with a general cost-benefit structure. Econometrica 40, 515528.Google Scholar
[7] Knudsen, N. C. and Stidham, S. (1976) Individual and social optimization in a birth-death congestion system with a general cost-benefit structure. NCSU-IE Tech. Rpt. No. 76-8, Department of Industrial Engineering, North Carolina State University, Raleigh.Google Scholar
[8] Lippman, S. A. (1975) Applying a new device in the optimization of exponential queueing systems. Operat. Res. 23, 687710.Google Scholar
[9] Lippman, S. A. and Stidham, S. Jr (1977) Individual versus social optimization in exponential congestion systems. Operat. Res. 25, 233247.Google Scholar
[10] McGill, J. T. (1969) Optimal control of queueing systems with variable number of exponential servers. Technical Report No. 123, Department of Operations Research and Statistics, Stanford University.Google Scholar
[11] Miller, B. L. (1969) A queueing reward system with several customer classes. Management Sci. 16, 234245.CrossRefGoogle Scholar
[12] Mitchell, W. (1973) Optimal service-rate selection in an M/G/l queue. SIAM J. Appl. Math. 24, 1935.Google Scholar
[13] Naor, P. (1969) On the regulation of queue size by levying tolls. Econometrica 37, 1524.Google Scholar
[14] Ross, S. M. (1970) Applied Probability Models with Optimization Applications. Holden-Day, San Francisco.Google Scholar
[15] Schäl, M. (1975) Conditions for optimality in dynamic programming and for the limit of n-stage optimal policies to be optimal. Z. Wahrscheinlichkeitsth. 32, 179296.Google Scholar
[16] Serfozo, R. F. (1976) Monotone optimal policies for Markov decision processes. Math. Prog. 6, North-Holland, 202215.Google Scholar
[17] Sobel, M. J. (1975) The optimality of full-service policies. School of Organization and Management, Yale University.Google Scholar
[18] Stidham, S. Jr (1970) On the optimality of single-server queueing systems. Operat. Res. 18, 708732.Google Scholar
[19] Stidham, S. Jr (1978) Socially and individually optimal control of arrivals to a GI/M/1 queue. Management Sci. 24, 15981610.Google Scholar
[20] Stidham, S. Jr and Prabhu, N. U. (1974) Optimal control of queueing systems. In Mathematical Methods in Queueing Theory. Lecture Notes in Economics and Mathematical Systems 98, Springer-Verlag, Berlin, 263294.Google Scholar
[21] Stoyan, D. (1977) Bounds and approximations in queueing through monotonicity and continuity. Operat. Res. 25, 851863.CrossRefGoogle Scholar
[22] Topkis, D. (1978) Minimizing a submodular function on a lattice. Operat. Res. 26, 305321.Google Scholar
[23] Veinott, A. F. (1965) Optimal policy in a dynamic, single-product, non-stationary inventory model with several demand classes. Operat. Res. 13, 761778.CrossRefGoogle Scholar
[24] Veinott, A. F. (1967) Unpublished class notes. Department of Operations Research, Stanford University.Google Scholar
[25] Winston, W. (1978) Optimality of monotonic policies for multiple-server exponential queueing systems with state-dependent arrival rates. Operat. Res. 26, 10891094.Google Scholar
[26] Yechiali, U. (1971) On optimal balking rules and toll charges in a GI/M/1 queueing process. Operat. Res. 19, 349370.Google Scholar
[27] Yechiali, U. (1972) Customers' optimal joining rules for the GI/M/s queue. Management Sci. 18, 434443.Google Scholar