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首页> 外文期刊>International journal of computational and applied mathematics >A Hybrid Markov Chain Model of Manpower Data
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A Hybrid Markov Chain Model of Manpower Data

机译：人力数据的混合马尔可夫链模型

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摘要

A hybrid model based on Markov chain and data interpolation is proposed for evaluating the manpower recruitment policy in higher learning institution. The model is developed and analysed on Excel spreadsheet. Based on the model, the new estimation of the states transition matrix for each category of manpower driven by interpolation technique is devised. The recruitment policy changes require some data needs modification while other data remains. This dataset are numerically intractable. But the revised transition matrix of Markov chain can be substituted by an interpolated data for which a revised transition probability matrix can be used as an equation solver to calculate mean time estimation for each category of manpower. The hybrid model results are then compared to the classical Markov chain result for both old and new policies by means of mean time estimation. Two scenarios were considered in the study; scenario 1 was based on historical data pattern between year 1999 - 2014 and scenario 2 was based on RMK 9 policies. The results showed the possibility average length of stay by position and probability of loss for both scenarios. The greater impact is expected for average length of stay of senior lecturers compared to other faculty position considering the new policy.
机译：基于马尔可夫链的混合模型和数据插值,提出了评估在高等学校人力资源招聘政策机构。在Excel电子表格。状态转移矩阵的估计每个类别的人力驱动的插值技术是发明。改变需要一些数据需要修改而其他数据仍然存在。数值棘手。马尔可夫链的转移矩阵代替由插值数据的可以使用修改后的转移概率矩阵作为一个方程解算器来计算平均时间估计为每个类别的人力。混合模型结果进行比较经典老和马尔可夫链结果新政策的同时估计。两个场景被认为在研究中;场景1是基于历史数据的模式1999年- 2014年和场景2在RMK 9日政策。可能性的平均停留时间的位置和概率的损失情况。大的影响预计的平均长度保持比其他的高级讲师教师职位考虑新政策。

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