Hostname: page-component-7dd5485656-c8zdz Total loading time: 0 Render date: 2025-10-31T17:13:38.202Z Has data issue: false hasContentIssue false
  • English
  • Français

Procedures for the evaluation of strategies for resource allocation in a stochastic environment

Published online by Cambridge University Press:  14 July 2016

K. D. Glazebrook
K. D. Glazebrook*
Affiliation:
University of Newcastle upon Tyne
*
∗ Postal address: Department of Mathematics and Statistics, University of Newcastle upon Tyne, NE1 7RU, UK.
Get access Rights & Permissions [Opens in a new window]

Abstract

Existing procedures for strategy evaluation based on earlier work by the author have led (inter alia) to procedures for the assessment of heuristics for stochastic scheduling and to the development of an approach to sensitivity analysis. Recent theoretical work has now led to the development of more effective evaluation procedures. The key results are reported here.

Keywords

GITTINS INDEXSTOCHASTIC MODELS FOR RESOURCE ALLOCATIONSTRATEGY EVALUATION

Information

Type
Short Communications
Information
Journal of Applied Probability , Volume 27 , Issue 1 , March 1990 , pp. 215 - 220
Copyright
Copyright © Applied Probability Trust 1990 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Bather, J. A. (1981) Randomized allocation of treatments in sequential experiments (with discussion). J. R. Statist. Soc. B 43, 265–292.Google Scholar
Bergman, S. W. and Gittins, J. C. (1985) Statistical Methods for Pharmaceutical Research Planning. Marcel Dekker, New York.Google Scholar
Bruno, J. and Hofri, M. (1975) On scheduling chains of jobs on one processor with limited preemption. SIAM J. Comput. 4, 478–490.Google Scholar
Gittins, J. C. and Glazebrook, K. D. (1977) On Bayesian models in stochastic scheduling. J. Appl. Prob. 14, 556–565.Google Scholar
Gittins, J. C. (1979) Bandit processes and dynamic allocation indices (with discussion). J. R. Statist. Soc. B 41, 148–177.Google Scholar
Glazebrook, K. D. (1978) On the optimal allocation of two or more treatments in a controlled clinical trial. Biometrika 65, 335–340.Google Scholar
Glazebrook, K. D. (1982) On the evaluation of suboptimal strategies for families of alternative bandit processes. J. Appl. Prob. 19, 716–722.Google Scholar
Glazebrook, K. D. (1987) Sensitivity analysis for stochastic scheduling problems. Math. Operat. Res. 12, 205–223.Google Scholar
Glazebrook, K. D. and Fay, N. A. (1988) Evaluating strategies for generalized bandit problems. Internat. J. Systems Sci. 19, 1605–1613.Google Scholar
Katehakis, M. N. and Veinott, A. F. (1987) The multi-armed bandit problem: decomposition and computation. Math. Operat. Res. 12, 262–268.CrossRefGoogle Scholar
Varaiya, P., Walrand, J. and Buyukkoc, C. (1985) Extensions of the multi-armed bandit problem: the discounted case. IEEE Trans. Autom. Control 30, 426–439.Google Scholar