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Continuous-time stochastic models of a multigrade population

Published online by Cambridge University Press:  14 July 2016

Sally I. McClean*
Affiliation:
New University of Ulster

Abstract

The continuous-time Markov model of a multigrade organization is extended in several ways. Firstly the internal transitions and the leaving process are generalized to a semi-Markov formulation which allows for the inclusion of well-authenticated leaving distributions such as the mixed exponential distribution. The previous assumption of Poisson recruitment is then generalized to allow for a time-dependent Poisson arrival distribution in which the instantaneous probability of an arrival is a mixture of exponential terms. Finally we extend the capital-related manpower model to describe a multigrade organization.

Type
Research Papers
Copyright
Copyright © Applied Probability Trust 1978 

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References

Bartholomew, D. J. (1959) Note on the measurement and prediction of labour turnover. J. R. Statist. Soc. A 122, 232239.Google Scholar
Bartholomew, D. J. (1971) The statistical approach to manpower planning. Statistician 20, 326.CrossRefGoogle Scholar
Bartholomew, D. J. (1973) Stochastic Models for Social Processes, 2nd edn. Wiley, New York and London.Google Scholar
Clowes, G. A. (1972) A dynamic model for the analysis of labour turnover. J. R. Statist. Soc. A 135, 242256.Google Scholar
Fix, E. and Neyman, J. (1951) A simple stochastic model of recovery relapse, death and loss of patients. Hum. Biol. 23, 205241.Google Scholar
Gani, J. (1963) Formulae for projecting enrolments and degrees awarded in universities. J. R. Statist. Soc. A 126, 400409.Google Scholar
Griffiths, D. A. (1972) A bivariate birth-death process which approximated to the spread of a disease involving a vector. J. Appl. Prob. 9, 6572.CrossRefGoogle Scholar
Herbst, P. G. (1963) Organizational commitment: a decision model. Acta Sociologica 7, 3445.CrossRefGoogle Scholar
Leslie, P. H. (1945) On the use of matrices in certain population mathematics. Biometrika 33, 183212.Google Scholar
Matis, J. H. and Hartley, H. O. (1971) Stochastic compartmental analysis: model and least squares estimation from time series data. Biometrics 27, 7797.Google Scholar
McClean, S. I. (1976a) The two stage model of personnel behaviour. J. R. Statist. Soc. A 139, 205217.Google Scholar
McClean, S. I. (1976b) A continuous-time population model with Poisson recruitment. J. Appl. Prob. 13, 348354.Google Scholar
McClean, S. I. (1976c) Some models for company growth. J. R. Statist. Soc. A 139, 501507.Google Scholar
Pollard, J. H. (1967) Hierarchical population models with Poisson recruitment. J. Appl. Prob. 4, 209213.Google Scholar
Raman, S. and Chiang, C. L. (1973) On a solution of the migration process and the application to a problem in epidemiology. J. Appl. Prob. 10, 718727.CrossRefGoogle Scholar
Rice, A. K., Hill, J. M. M. and Trist, E. L. (1950) The representation of labour turnover as a social process. Hum. Relations 3, 349381.Google Scholar
Steindhl, P. (1965) Random Processes and the Growth of Firms. Griffin, London.Google Scholar
Young, A. and Almond, G. (1961) Predicting distributions of staff. Computer J. 3, 246250.Google Scholar
Young, A. and Abodunde, T. T. (1976) Personnel recruitment policies and long-term production planning Operat. Res. Quart. To appear.Google Scholar