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

IF 1 4区 数学 Q3 STATISTICS & PROBABILITY
E O Ossai, M S Madukaife, A U Udom, U C Nduka, T E Ugah
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Abstract

In this paper, extended Markov manpower models are formulated by incorporating a new class of members of a departmentalized manpower system in a homogeneous Markov manpower model. The new class, called limbo class, admits members of the system who exit to a limbo state for possible re-engagement in the active class. This results to two channels of recruitment: one from the limbo class and another from the outside environment. The idea is motivated by the need to preserve trained and experienced individuals who could be lost in times of financial crises or due to contract completion. The control aspect of the manpower structure under the extended models are examined. Under suitable stochastic condition for the flow matrices, it is proved that the maintainability of the manpower structure through promotion does not depend on the structural form of the limbo class when the system is expanding with priority on recruitment from outside environment, nor on the structural form of the active class when the system is shrinking with priority on recruitment from the limbo class. Necessary and sufficient conditions for maintainability of the manpower structure through recruitment in the case of expanding systems are also established with proofs.

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部门化马尔可夫人力资源框架下优先投入对人力资源控制的影响。
本文通过在齐次马尔可夫人力模型中加入部门化人力系统中一类新的成员,建立了扩展马尔可夫人力模型。这个新类被称为limbo类,它允许退出到limbo状态的系统成员重新参与到活跃类中。这就形成了两种招聘渠道:一种来自边缘阶级,另一种来自外部环境。这一想法的动机是,需要保留训练有素、经验丰富的员工,以免在金融危机或合同完成时流失。研究了扩展模型下的人力结构控制问题。在合适的流矩阵随机条件下,证明了晋升人力结构的可维护性不依赖于系统扩张优先从外部环境招聘时的边缘阶层结构形式,也不依赖于系统收缩优先从边缘阶层招聘时的活动阶层结构形式。并提出了在系统扩张型情况下通过招聘维持人力结构可维护性的充分必要条件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
1.70
自引率
0.00%
发文量
58
审稿时长
6-12 weeks
期刊介绍: Methodology and Computing in Applied Probability will publish high quality research and review articles in the areas of applied probability that emphasize methodology and computing. Of special interest are articles in important areas of applications that include detailed case studies. Applied probability is a broad research area that is of interest to many scientists in diverse disciplines including: anthropology, biology, communication theory, economics, epidemiology, finance, linguistics, meteorology, operations research, psychology, quality control, reliability theory, sociology and statistics. The following alphabetical listing of topics of interest to the journal is not intended to be exclusive but to demonstrate the editorial policy of attracting papers which represent a broad range of interests: -Algorithms- Approximations- Asymptotic Approximations & Expansions- Combinatorial & Geometric Probability- Communication Networks- Extreme Value Theory- Finance- Image Analysis- Inequalities- Information Theory- Mathematical Physics- Molecular Biology- Monte Carlo Methods- Order Statistics- Queuing Theory- Reliability Theory- Stochastic Processes
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