Hostname: page-component-848d4c4894-hfldf Total loading time: 0 Render date: 2024-06-02T02:31:07.962Z Has data issue: false hasContentIssue false

Using fuzzy sets in manpower planning

Published online by Cambridge University Press:  14 July 2016

M. A. Guerry*
Affiliation:
Vrije Universiteit Brussel
*
Postal address: Center for Manpower Planning and Studies, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussel, Belgium. Email address: maguerry@vub.ac.be

Abstract

In this paper, given personnel distributions that are not attainable, we introduce the grade of attainability in order to measure the degree to which there exists a similar distribution that is attainable. For constant size systems controlled by recruitment, properties of the most similar distribution to a given distribution are formulated.

Type
Research Papers
Copyright
Copyright © Applied Probability Trust 1999 

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.)

References

Bartholomew, D. J. (1982). Stochastic Models for Social Processes, 3rd edn. Wiley, Chichester.Google Scholar
Bartholomew, D. J., Forbes, A. F., and McClean, S. I. (1991). Statistical Techniques for Manpower Planning, 2nd edn. Wiley, Chichester.Google Scholar
Bellman, R. E., and Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Management Sci. 17, 141164.Google Scholar
Davies, G. S. (1982). Control of grade sizes in a partially stochastic Markov manpower model. J. Appl. Prob. 19, 439443.CrossRefGoogle Scholar
Guerry, M. A. (1991). Maintainability and Attainability in Manpower Systems. , Center for Manpower Planning, Vrije Universiteit Brussel, Belgium.Google Scholar
Guerry, M. A. (1997). The grade of attainability for personnel distributions. STOO/280, Centrum voor Statistiek, Operationeel Onderzoek en Wiskunde toegepast op de Humane Wetenschappen, Vrije Universiteit Brussel, Belgium.Google Scholar
Kacprzyk, J., and Orlovski, S. A. (1987). Optimization Models Using Fuzzy Sets and Possibility Theory. Kluwer, Dordrecht.Google Scholar
Negoita, C. V. (1976). Fuzzy models for social processes. In Systems Theory in the Social Sciences, ed. Bossely, H., Klaczko, S. and Muller, N. Birkhäuser, Boston, MA, pp. 283291.CrossRefGoogle Scholar
Novak, V. (1989). Fuzzy Sets and Their Applications. Hilger, Bristol.Google Scholar
Schwartz, J. (1962). The pernicious influence of mathematics on science. In Logic, Methodology and Philosophy of Science, ed. Nagel, E., Suppes, P. and Tarski, A., Stanford University Press, Stanford, CA.Google Scholar
Skala, H. J. (1976). Fuzzy concepts: logic, motivation, application. In Systems Theory in the Social Sciences, ed. Bossely, H., Klaczko, S. and Muller, N. Birkhäuser, Boston, MA, p. 292306.Google Scholar
Vajda, S. (1978). Mathematics of Manpower Planning. Wiley, London.Google Scholar
Vassiliou, P.-C. G., and Georgiou, A. C. (1990). Assymptotically attainable structures in non-homogeneous Markov systems. Operat. Res. 38, 537545.CrossRefGoogle Scholar
Zadeh, L. A. (1976). A fuzzy-algorithmic approach to the definition of complex or imprecise concepts. In Systems Theory in the Social Sciences. Birkhauser, Boston, MA, pp. 202282.Google Scholar
Zimmerman, H. J. (1983). Using fuzzy sets in operational research. European J. Operat. Res. 13, 201216.Google Scholar
Zimmerman, H. J. (1985). Fuzzy Set Theory and its Applications. Kluwer, Dordrecht.Google Scholar