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Discrete Time Hybrid Semi-Markov Models in Manpower Planning

Author

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  • Brecht Verbeken

    (Department of Business Technology and Operations, Vrije Universiteit Brussel, Pleinlaan, 2, 1050 Brussels, Belgium)

  • Marie-Anne Guerry

    (Department of Business Technology and Operations, Vrije Universiteit Brussel, Pleinlaan, 2, 1050 Brussels, Belgium)

Abstract

Discrete time Markov models are used in a wide variety of social sciences. However, these models possess the memoryless property, which makes them less suitable for certain applications. Semi-Markov models allow for more flexible sojourn time distributions, which can accommodate for duration of stay effects. An overview of differences and possible obstacles regarding the use of Markov and semi-Markov models in manpower planning was first given by Valliant and Milkovich (1977). We further elaborate on their insights and introduce hybrid semi-Markov models for open systems with transition-dependent sojourn time distributions. Hybrid semi-Markov models aim to reduce model complexity in terms of the number of parameters to be estimated by only taking into account duration of stay effects for those transitions for which it is useful. Prediction equations for the stock vector are derived and discussed. Furthermore, the insights are illustrated and discussed based on a real world personnel dataset. The hybrid semi-Markov model is compared with the Markov and the semi-Markov models by diverse model selection criteria.

Suggested Citation

  • Brecht Verbeken & Marie-Anne Guerry, 2021. "Discrete Time Hybrid Semi-Markov Models in Manpower Planning," Mathematics, MDPI, vol. 9(14), pages 1-13, July.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:14:p:1681-:d:596018
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    References listed on IDEAS

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    Cited by:

    1. Mark Kiermayer & Christian Wei{ss}, 2022. "Neural calibration of hidden inhomogeneous Markov chains -- Information decompression in life insurance," Papers 2201.02397, arXiv.org.
    2. E. O. Ossai & M. S. Madukaife & A. U. Udom & U. C. Nduka & T. E. Ugah, 2023. "Effects of Prioritized Input on Human Resource Control in Departmentalized Markov Manpower Framework," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-19, March.

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