An extended and tractable approach on the convergence problem of the mixed push–pull manpower model

Guerry, Marie-Anne; Feyter, Tim De

doi:10.1016/j.amc.2011.03.123

Cited by 12 publications

(4 citation statements)

References 28 publications

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“…The work of Seal ( 1945 ) is a pioneering one in the application of Markov theory in Manpower planning. Though extensions of the application of Markov chain theory in manpower planning now abound, such as in semi-Markov manpower models (McClean et al 1998 ; Yadavalli and Natarajan 2001 ), hybrid manpower models (De Feyter 2007 ; Guerry and De Feyter 2011 ; Verbeken and Guerry 2021 ), Markov manpower models remain very relevant because of their property of being comparatively simple and tractable. These properties are required in practical implementation of manpower models (Barsnet and Ellison 1998 ), and are platforms for the extensions and other new grounds in mathematical manpower modelling.…”

Section: Introductionmentioning

confidence: 99%

Effects of Prioritized Input on Human Resource Control in Departmentalized Markov Manpower Framework

Ossai

Madukaife

Udom

et al. 2023

Methodol Comput Appl Probab

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Section: Introductionmentioning

confidence: 99%

Effects of Prioritized Input on Human Resource Control in Departmentalized Markov Manpower Framework

Ossai

Madukaife

Udom

et al. 2023

Methodol Comput Appl Probab

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“…Three basic stochastic models that have been employed more in statistical manpower modelling are Markov, semi-Markov and renewal models [5,2,6,7,8,9,10,11]. The choice and treatment of any of these models are governed by a number of factors, which include: the nature of transitions and promotion flow -whether it is of 'push' or 'pull' type [12,13,14], discrete or continuous time space [15,16,17], system homogeneity or non-homogeneity [18,19,8,20], model tractability, descriptive ability and practicability [21]. Though Markov and semi-Markov models are push types, they have relative advantages over one another.…”

Section: Introductionmentioning

confidence: 99%

“…One of such structural extensions is the non-active or limbo class [25], which holds members who face wastage, but are still useful, in a non-active and limbo position. According to Guerry [13,14], wastage analysis is very essential in manpower planning because wastage governs, to a large extent, the need for promotion and recruitment. According to Ossai et al [25], the inclusion of the limbo class is a form of wastage control.…”

Section: Introductionmentioning

confidence: 99%

Steady State Conditions in Tractable Markov Manpower Model for an Extended Manpower System

Ossai

2023

AJPAS

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In this work, a formulation of manpower structure in discrete-time homogeneous Markov model is done for a multilevel manpower system. The structure of the manpower system is first extended in a departmentalized framework and the features of the extended structure utilized to create a scenario of personnel membership in three classes: the active, non-active and external classes. This allows for the inclusion of different units of the system in the model. Specifically, a pool of members in absorbing states with respect to intra-class transitions is included, which forms a second channel of recruitment. The first channel of recruitment is from the external class and all recruits go to the active class. All states of the active class are non-absorbing and give rise to various intra-class and inter-class transitions. Different probability matrices that form the main components of the Markov manpower model are constructed from probabilities of these transitions. One-step steady state condition on the manpower structure is considered, based on the formulated model, and established to be invariant with respect to varying proportion of recruits into the system.

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“…A manpower system can be represented by a number of functional models, see, for example, [12][13][14][15], of which Markov model is one. In a classical Markov manpower model (CMMM) only the observable classes are considered, where personnel inter-class transition probabilities are realized according to a Markov chain [16].…”

Section: Introductionmentioning

confidence: 99%

Likelihood Ratio Search Procedure for Optimum Number of States in a Hidden Markov Manpower Model

Ossai

Madukaife²

2022

AJPAS

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This paper aims at obtaining the optimum number of states for a hidden Markov manpower model, which, hitherto, has been chosen arbitrarily. A search procedure that attains this optimum number after a few steps across a series of N hidden Markov manpower models is proposed. The likelihood ratio statistic is employed to conduct pairwise model comparison tests on the N hidden Markov manpower models ordered according to their level of parsimony. The illustration shows the usefulness of the procedure in choosing the right number of states for a hidden Markov manpower model to avoid wrong specification of such models. The proposed procedure can be useful in other areas of research, such as in biological, medical and social sciences, where application of hidden Markov model may require the determination of number of hidden states based on unobserved data with latent heterogeneity. The procedure has a straightforward formulation and its application in other areas requires mainly the adaptation of the model specifications for the new area’s system dynamics.

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An extended and tractable approach on the convergence problem of the mixed push–pull manpower model

Cited by 12 publications

References 28 publications

Effects of Prioritized Input on Human Resource Control in Departmentalized Markov Manpower Framework

Effects of Prioritized Input on Human Resource Control in Departmentalized Markov Manpower Framework

Steady State Conditions in Tractable Markov Manpower Model for an Extended Manpower System

Likelihood Ratio Search Procedure for Optimum Number of States in a Hidden Markov Manpower Model

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