Modelling production uncertainties using the adaptive neuro-fuzzy inference system
Production throughput measures the performance and behaviour of a production system. Production throughput modelling is complex because of uncertainties in the production line. This study examined the potential application of the adaptive neuro-fuzzy inference system (ANFIS) to modelling the through...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Stellenbosch University
2015-05-01
|
Series: | South African Journal of Industrial Engineering |
Subjects: | |
Online Access: | http://sajie.journals.ac.za/pub/article/view/560 |
id |
doaj-8d54d545d04248808926c34b87ef9c72 |
---|---|
record_format |
Article |
spelling |
doaj-8d54d545d04248808926c34b87ef9c722020-11-25T01:47:48ZengStellenbosch UniversitySouth African Journal of Industrial Engineering1012-277X2224-78902015-05-0126122423410.7166/26-1-560Modelling production uncertainties using the adaptive neuro-fuzzy inference systemAzizi, Amir0Ali, Amir Yazid b. Ali1Loh Wei Ping2University of MalaysiaUniversity of Science MalaysiaUniversity of Science MalaysiaProduction throughput measures the performance and behaviour of a production system. Production throughput modelling is complex because of uncertainties in the production line. This study examined the potential application of the adaptive neuro-fuzzy inference system (ANFIS) to modelling the throughput of production under five significant production uncertainties: scrap, setup time, break time, demand, and lead time of manufacturing. The effects of these uncertainties on the production of floor tiles were studied by performing 104 observations on the production uncertainties over 104 weeks, based on a weekly production plan in a tile manufacturing industry. The results of the ANFIS model were compared with the multiple linear regression (MLR) model. The results showed that the ANFIS model was capable of forecasting production throughput under uncertainty with higher accuracy than was the MLR model, indicated by an R-squared of 98 per cent. http://sajie.journals.ac.za/pub/article/view/560production throughputNeuro-Fuzzyuncertainty |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Azizi, Amir Ali, Amir Yazid b. Ali Loh Wei Ping |
spellingShingle |
Azizi, Amir Ali, Amir Yazid b. Ali Loh Wei Ping Modelling production uncertainties using the adaptive neuro-fuzzy inference system South African Journal of Industrial Engineering production throughput Neuro-Fuzzy uncertainty |
author_facet |
Azizi, Amir Ali, Amir Yazid b. Ali Loh Wei Ping |
author_sort |
Azizi, Amir |
title |
Modelling production uncertainties using the adaptive neuro-fuzzy inference system |
title_short |
Modelling production uncertainties using the adaptive neuro-fuzzy inference system |
title_full |
Modelling production uncertainties using the adaptive neuro-fuzzy inference system |
title_fullStr |
Modelling production uncertainties using the adaptive neuro-fuzzy inference system |
title_full_unstemmed |
Modelling production uncertainties using the adaptive neuro-fuzzy inference system |
title_sort |
modelling production uncertainties using the adaptive neuro-fuzzy inference system |
publisher |
Stellenbosch University |
series |
South African Journal of Industrial Engineering |
issn |
1012-277X 2224-7890 |
publishDate |
2015-05-01 |
description |
Production throughput measures the performance and behaviour of a production system. Production throughput modelling is complex because of uncertainties in the production line. This study examined the potential application of the adaptive neuro-fuzzy inference system (ANFIS) to modelling the throughput of production under five significant production uncertainties: scrap, setup time, break time, demand, and lead time of manufacturing. The effects of these uncertainties on the production of floor tiles were studied by performing 104 observations on the production uncertainties over 104 weeks, based on a weekly production plan in a tile manufacturing industry. The results of the ANFIS model were compared with the multiple linear regression (MLR) model. The results showed that the ANFIS model was capable of forecasting production throughput under uncertainty with higher accuracy than was the MLR model, indicated by an R-squared of 98 per cent.
|
topic |
production throughput Neuro-Fuzzy uncertainty |
url |
http://sajie.journals.ac.za/pub/article/view/560 |
work_keys_str_mv |
AT aziziamir modellingproductionuncertaintiesusingtheadaptiveneurofuzzyinferencesystem AT aliamiryazidbali modellingproductionuncertaintiesusingtheadaptiveneurofuzzyinferencesystem AT lohweiping modellingproductionuncertaintiesusingtheadaptiveneurofuzzyinferencesystem |
_version_ |
1725014514226692096 |