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On Bayesian models in stochastic scheduling

Published online by Cambridge University Press:  14 July 2016

J. C. Gittins  and
K. D. Glazebrook
J. C. Gittins
Affiliation:
University of Oxford
K. D. Glazebrook
Affiliation:
University of Newcastle upon Tyne
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Abstract

The D.A.I. theorem of Gittins and Jones has proved a powerful tool in solving sequential statistical problems. A generalisation of this theorem is presented. This generalisation enables us to solve certain stochastic scheduling problems where the items or jobs to be scheduled have random times to completion, the random times having distributions dependent upon parameters to which prior distributions are allocated. Such problems are of interest in many areas where scheduling is important.

Keywords

DYNAMIC ALLOCATION INDEXBANDIT PROCESSESSTOCHASTIC SCHEDULING
Type
Research Papers
Information
Journal of Applied Probability , Volume 14 , Issue 3 , September 1977 , pp. 556 - 565
Copyright
Copyright © Applied Probability Trust 1977 

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References

Cranston, R. W. (1975) First experiences with a ranking method for portfolio selection in applied research. R. and D. Mgmt. 5, 88–92.CrossRefGoogle Scholar
Gittings, J. C. and Jones, D. M. (1972) A dynamic allocation index for the sequential design of experiments. Progress in Statistics (Proceedings of the European Meeting of Statisticians), North-Holland, Amsterdam.Google Scholar
Gittins, J. C. and Nash, P. (1974) Scheduling, queues and dynamic allocation indices. Proc. European Meeting of Statisticians, Prague. Google Scholar
Glazebrook, K. D. (1976a) Stochastic Scheduling. Ph.D. Thesis, Cambridge University.Google Scholar
Glazebrook, K. D. (1976b) Stochastic scheduling with order constraints. Internat. J. Systems Sci. 7, 657–666.Google Scholar
Glazebrook, K. D. (1976C) A profitability index for alternative research projects. Omega 4, 79–83.Google Scholar
Nash, P. (1973) Optimal Allocation of Resources Between Research Projects. Ph.D. Thesis, Cambridge University.Google Scholar
Sevcik, K. C. (1972) The use of service time distributions in scheduling. Technical Report CSRG–14, University of Toronto.Google Scholar