Productioon uncertainties modelling by Bayesian inference using Gibbs sampling
Analysis by modelling production throughput is an efficient way to provide information for production decision-making. Observation and investigation based on a real-life tile production line revealed that the five main uncertain variables are demand rate, breakdown time, scrap rate, setup time, and...
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Stellenbosch University
2015-11-01
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doaj-b6d3b4b576034c33a837e7a82007c86e2020-11-24T22:44:48ZengStellenbosch UniversitySouth African Journal of Industrial Engineering1012-277X2224-78902015-11-01263274010.7166/26-3-572Productioon uncertainties modelling by Bayesian inference using Gibbs samplingAzizi, Amir0Ali, Amir Yazid b.1Loh Wei Ping2Mohammadzadeh, Mohsen3University of MalaysiaUniversity of Science MalaysiaUniversity of Science MalaysiaTarbiat Modares UniversityAnalysis by modelling production throughput is an efficient way to provide information for production decision-making. Observation and investigation based on a real-life tile production line revealed that the five main uncertain variables are demand rate, breakdown time, scrap rate, setup time, and lead time. The volatile nature of these random variables was observed over a specific period of 104 weeks. The processes were sequential and multi-stage. These five uncertain variables of production were modelled to reflect the performance of overall production by applying Bayesian inference using Gibbs sampling. The application of Bayesian inference for handling production uncertainties showed a robust model with 2.5 per cent mean absolute percentage error. It is recommended to consider the five main uncertain variables that are introduced in this study for production decision-making. The study proposes the use of Bayesian inference for superior accuracy in production decision-making. http://sajie.journals.ac.za/pub/article/view/572uncertaintythroughputBayesian |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Azizi, Amir Ali, Amir Yazid b. Loh Wei Ping Mohammadzadeh, Mohsen |
spellingShingle |
Azizi, Amir Ali, Amir Yazid b. Loh Wei Ping Mohammadzadeh, Mohsen Productioon uncertainties modelling by Bayesian inference using Gibbs sampling South African Journal of Industrial Engineering uncertainty throughput Bayesian |
author_facet |
Azizi, Amir Ali, Amir Yazid b. Loh Wei Ping Mohammadzadeh, Mohsen |
author_sort |
Azizi, Amir |
title |
Productioon uncertainties modelling by Bayesian inference using Gibbs sampling |
title_short |
Productioon uncertainties modelling by Bayesian inference using Gibbs sampling |
title_full |
Productioon uncertainties modelling by Bayesian inference using Gibbs sampling |
title_fullStr |
Productioon uncertainties modelling by Bayesian inference using Gibbs sampling |
title_full_unstemmed |
Productioon uncertainties modelling by Bayesian inference using Gibbs sampling |
title_sort |
productioon uncertainties modelling by bayesian inference using gibbs sampling |
publisher |
Stellenbosch University |
series |
South African Journal of Industrial Engineering |
issn |
1012-277X 2224-7890 |
publishDate |
2015-11-01 |
description |
Analysis by modelling production throughput is an efficient way to provide information for production decision-making. Observation and investigation based on a real-life tile production line revealed that the five main uncertain variables are demand rate, breakdown time, scrap rate, setup time, and lead time. The volatile nature of these random variables was observed over a specific period of 104 weeks. The processes were sequential and multi-stage. These five uncertain variables of production were modelled to reflect the performance of overall production by applying Bayesian inference using Gibbs sampling. The application of Bayesian inference for handling production uncertainties showed a robust model with 2.5 per cent mean absolute percentage error. It is recommended to consider the five main uncertain variables that are introduced in this study for production decision-making. The study proposes the use of Bayesian inference for superior accuracy in production decision-making. |
topic |
uncertainty throughput Bayesian |
url |
http://sajie.journals.ac.za/pub/article/view/572 |
work_keys_str_mv |
AT aziziamir productioonuncertaintiesmodellingbybayesianinferenceusinggibbssampling AT aliamiryazidb productioonuncertaintiesmodellingbybayesianinferenceusinggibbssampling AT lohweiping productioonuncertaintiesmodellingbybayesianinferenceusinggibbssampling AT mohammadzadehmohsen productioonuncertaintiesmodellingbybayesianinferenceusinggibbssampling |
_version_ |
1725690354359861248 |