Application of Markov Chain Model to Corporate Manpower Planning: A Nigeria Local Government Hub Example

2013 ◽  
Vol 824 ◽  
pp. 514-526 ◽  
Author(s):  
A.C. Igboanugo
Keyword(s):  
Markov Chain ◽  
Local Government ◽  
Markov Chain Model ◽  
Manpower Planning ◽  
Chain Model ◽  
Government Organizations ◽  
Absorbing Markov Chain ◽  
Long Run ◽  
Definite Pattern ◽  
Absorbing States

A corporate manpower planning study, seeking to gain insight into, and hence, attempt tounwrapthe wider meanings of a long-run manpower practice inherent in a set of data obtained from one of the 774 Local Government Organizations in Nigeria, was conducted. The data which spanned over a period of twenty years, relate to six states recruitment, staff stock, training, interdiction, wastage, and retirement and, in particular were found to possess Markov properties, especially stochastic regularity, and therefore had absorbing Markov Chain model fitted into the set. Our results suggest that staff habituate substantial number of times (47) among non-absorbing states before subsequent absorption into any of the two absorbing states. And, again, 52% of the workforce gracefully attain retirement while 48% regrettably get wasted. Agreeably, it seemed that the absorbing Markov Chain model employed has established a definite pattern of manpower flow in the organization as a sure-thing principle rather than a chance mechanism.

Evaluating basketball player’s rotation line-ups performance via statistical markov chain modelling

2021 ◽  
pp. 174795412110090
Author(s):  
Pavlos Kolias ◽  
Nikolaos Stavropoulos ◽  
Alexandra Papadopoulou ◽  
Theodoros Kostakidis
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Specific Rotation ◽  
Multiple Regression Model ◽  
Chain Model ◽  
Know How ◽  
Basketball Game ◽  
Long Run ◽  
Rotation Line ◽  
Rating Category

Coaches in basketball often need to know how specific rotation line-ups perform in either offense or defense and choose the most efficient formation, according to their specific needs. In this research, a sample of 1131 ball possession phases of Greek Basket League was utilized, in order to estimate the offensive and defensive performance of each formation. Offensive and defensive ratings for each formation were calculated as a function of points scored or received, respectively, over possessions, where possessions were estimated using a multiple regression model. Furthermore, a Markov chain model was implemented to estimate the probabilities of the associated formation’s performance in the long run. The model could allow us to distinguish between overperforming and underperforming formations and revealed the probabilities over the evolution of the game, for each formation to be in a specific rating category. The results indicated that the most dominant formation, in terms of offense, is Point Guard-Point Guard-Small Forward-Power Forward-Center, while defensively schema Point Guard-Shooting Guard-Small Forward-Center-Center had the highest rating. Such results provide information, which could operate as a supplementary tool for the coach’s decisions, related to which rotation line-up patterns are mostly suitable during a basketball game.

scholarly journals Application of Markov chain in students’ assessment and performance: a case study of School of Mathematical Sciences, one of the public university in Malaysia

2021 ◽  
Vol 36 ◽  
pp. 01004
Author(s):  
Khairun Husna Yahaya ◽  
Husna Hasan
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Essential Element ◽  
Target Population ◽  
Chain Model ◽  
Undergraduate Enrollment ◽  
Long Run ◽  
Mathematical Sciences ◽  
And Performance ◽  
And Gender

The quality education is an essential element in economic, political and social development of any country. Therefore, enrollment forecasting is needed in higher education to assist the universities in the preparation of their educational frameworks including budgeting, provide all necessary facilities and planning the overall short and long term goals. This research study the pattern of students’ assessment and their academic performance in School of Mathematical Sciences, Universiti Sains Malaysia. The target population is all undergraduate enrollment from 2016/2017 until 2018/2019 sessions. An absorbing Markov chain model is applied to study the absorption, retention and repetitive rates of the students by the academic programs and gender. The fundamental matrix is constructed to determine the expected duration of schooling before graduating. The enrollment projection is also estimated to study the probability of the performances of the students in the long run. In summary, this research addresses on the use of Markov chain model to describe the stochastic pattern of the enrollment and assessment of the students.

scholarly journals An Absorbing Markov Chain Model to Predict Dairy Cow Calving Time

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6490
Author(s):  
Swe Zar Maw ◽  
Thi Thi Zin ◽  
Pyke Tin ◽  
Ikuo Kobayashi ◽  
Yoichiro Horii
Keyword(s):  
Markov Chain ◽  
Dairy Cow ◽  
Markov Chain Model ◽  
Dairy Farm ◽  
Transient State ◽  
Machine Learning Techniques ◽  
Chain Model ◽  
Expected Time ◽  
Absorbing Markov Chain ◽  
Learning Techniques

Abnormal behavioral changes in the regular daily mobility routine of a pregnant dairy cow can be an indicator or early sign to recognize when a calving event is imminent. Image processing technology and statistical approaches can be effectively used to achieve a more accurate result in predicting the time of calving. We hypothesize that data collected using a 360-degree camera to monitor cows before and during calving can be used to establish the daily activities of individual pregnant cows and to detect changes in their routine. In this study, we develop an augmented Markov chain model to predict calving time and better understand associated behavior. The objective of this study is to determine the feasibility of this calving time prediction system by adapting a simple Markov model for use on a typical dairy cow dataset. This augmented absorbing Markov chain model is based on a behavior embedded transient Markov chain model for characterizing cow behavior patterns during the 48 h before calving and to predict the expected time of calving. In developing the model, we started with an embedded four-state Markov chain model, and then augmented that model by adding calving as both a transient state, and an absorbing state. Then, using this model, we derive (1) the probability of calving at 2 h intervals after a reference point, and (2) the expected time of calving, using their motions between the different transient states. Finally, we present some experimental results for the performance of this model on the dairy farm compared with other machine learning techniques, showing that the proposed method is promising.

scholarly journals Markov Chain Model Development for Forecasting Air Pollution Index of Miri, Sarawak

2019 ◽  
Vol 11 (19) ◽  
pp. 5190 ◽  
Author(s):  
Nurul Nnadiah Zakaria ◽  
Mahmod Othman ◽  
Rajalingam Sokkalingam ◽  
Hanita Daud ◽  
Lazim Abdullah ◽  
...  
Keyword(s):  
Air Pollution ◽  
Markov Chain ◽  
Air Quality ◽  
Markov Chain Model ◽  
Model Development ◽  
Pollution Index ◽  
Pollution Level ◽  
Chain Model ◽  
Air Pollution Index ◽  
Long Run

A Markov chain is commonly used in stock market analysis, manpower planning, and in many other areas because of its efficiency in predicting long run behavior. However, the Air Quality Index (AQI) suffers from not using a Markov chain in its forecasting approach. Therefore, this paper proposes a simple forecasting tool to predict the future air quality with a Markov chain model. The proposed method introduces the Markov chain as an operator to evaluate the distribution of the pollution level in the long term. Initial state vector and state transition probability were used in forecasting the behavior of Air Pollution Index (API) that has been obtained from the observed frequency for one state shift to another. The study explores that regardless of the present status of API, in the long run, the index shows a probability of 0.9231 for a good state, and a moderate and unhealthy state with a probability of 0.0722 and 0.0037, while for very unhealthy and hazardous states a probability of 0.0001 and 0.0009. The outcome of this study reveals that the model development could be used as a forecasting method that able to help government to project a prevention action plan during hazy weather.

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