An absorbing Markov chain model for production systems with rework and scrapping

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.

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An enhanced absorbing Markov chain model for predicting TAIEX Index Futures

Communication in Statistics- Theory and Methods ◽

10.1080/03610926.2017.1300281 ◽

2017 ◽

Vol 47 (1) ◽

pp. 133-146 ◽

Cited By ~ 2

Author(s):

Wen-Tso Huang ◽

Cheng-Chang Lu

Keyword(s):

Markov Chain ◽

Markov Chain Model ◽

Chain Model ◽

Index Futures ◽

Absorbing Markov Chain

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An Absorbing Markov Chain Model to Predict Dairy Cow Calving Time

Sensors ◽

10.3390/s21196490 ◽

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.

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