Manpower planning using Markov Chain model

Saad, Syafawati Ab.; Adnan, Farah Adibah; Ibrahim, Haslinda; Rahim, Rahela Abdul

doi:10.1063/1.4887748

Cited by 5 publications

(5 citation statements)

References 3 publications

(6 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then there is a transition probability denoted P ij , and there is no dependence on the probability state of the last state. [29] There is a conditional opportunity that exists in a process if it is in a state i to j after an n-step transition occurs, then it becomes:[28]…”

Section: Calculations With Markov Chainmentioning

confidence: 99%

Markov Chain Method in Calculation of Personnel Recruitment Needs

Setyaningrum¹,

Abdurachman²,

Warnars³

et al. 2022

TEM Journal

View full text Add to dashboard Cite

The selection of military personnel through the recruitment stage must be under planning. The calculation in this study uses the Markov Chain method with an error calculation which is the result of a comparison with the conditions of the number of recruits in the previous period so that the error value is known and can be used in the next period using a computerized system. This study provides an alternative to determining the error value in predicting the number of recruits more easily and based on previous data by considering various aspects, such as rank, expertise, and job criteria required.

show abstract

Section: Calculations With Markov Chainmentioning

confidence: 99%

Markov Chain Method in Calculation of Personnel Recruitment Needs

Setyaningrum¹,

Abdurachman²,

Warnars³

et al. 2022

TEM Journal

View full text Add to dashboard Cite

show abstract

“…In Malaysia, Markov chain technique was commonly used in manpower planning. Saad et al was introduced this technique to investigate the track movements of lecturers in universities (Saad et al, 2014). Zhou et al noticed that Markov Chain is the most significant techniques and constraints for the good prediction of the probability of bikes rental and returns in a bike sharing system at each station in China (Zhou et al, 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Air Pollution Index in Klang by Markov Chain Model

Zakaria¹,

Sokkalingam²,

Daud³

et al. 2019

IJEAT

View full text Add to dashboard Cite

The main purpose of analyze future air quality is to maintain the environment in good and healthy condition. Current techniques applied to forecast the air pollution index were ARIMA, SARIMA, Artificial Neural Network, Fuzzy Time Series, Machine Learning, etc. Thus, each technique has its own advantages and disadvantages in the variables, model selection and model accuracy determination. This study aims to forecast air pollution index by developing a Markov Chain model in Klang district, Selangor state which is one of the most polluted area in Malaysia. The Markov Chain model development is a stochastic process sequence that depends on the previous successive event in time. In this model development, state transition matrix and probability are the main concept in determine the future behavior of Air Pollution Index which depends on the present state of the process. The result shows that the developed model is a simple and good performance model that will forecast and evaluate the distribution of the pollution level in long term.

show abstract

“…Choji et al [7] have employed the MC model for predicting the long run of share prices for two banks in Nigeria. Saad et al [8] have investigated the track movements of lecturers in universities using the MC model. In [9], a discrete-time MC model has been applied for analyzing job transitions in Mexico.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Information Criterion for Fitting the Optimum Order of Markov Chain Models: Methodology and Application to Air Pollution Data

et al. 2022

View full text Add to dashboard Cite

The analysis of air pollution behavior is becoming crucial, where information on air pollution behavior is vital for managing air quality events. Many studies have described the stochastic behavior of air pollution based on the Markov chain (MC) models. Fitting the optimum order of MC models is essential for describing the stochastic process. However, uncertainty remains concerning the optimum order of such models for representing and characterizing air pollution index (API) data. In this study, the optimum order of the MC models for hourly and daily API sequences from seven stations in the central region of Peninsular Malaysia is identified, based on the Bayesian information criteria (BIC), contributing to exploring an adequate explanation of the probabilistic dependence of air pollution. A summary of the statistics for the API was calculated prior to the analysis. The Markov property and the divergence for the empirically estimated transition matrix of an MC sequence are also investigated. It is found from the analysis that the optimum order varies from one station to another. At most stations, for both observed and simulated API data, the second and third orders of the MC models are found to be optimum for hourly API occurrences, while the first-order MC is found to be most fitting for describing the dynamics of the daily API. Overall, fitting the optimum order of the MC model for the API data sequence captured the delay effect of air pollution. Accordingly, we concluded that the air quality standard lies within controllable limits, except for some infrequent occurrences of API values exceeding the unhealthy level.

show abstract

Manpower planning using Markov Chain model

Cited by 5 publications

References 3 publications

Markov Chain Method in Calculation of Personnel Recruitment Needs

Markov Chain Method in Calculation of Personnel Recruitment Needs

Forecasting Air Pollution Index in Klang by Markov Chain Model

Bayesian Information Criterion for Fitting the Optimum Order of Markov Chain Models: Methodology and Application to Air Pollution Data

Contact Info

Product

Resources

About