An Adaptive Neuro-Fuzzy Inference System for a Dynamic Production Environment under Uncertainties

Throughput modelling evaluates the performance and behaviour of the production systems. This study examined the potential application of Adaptive Neuro-Fuzzy Inference System (ANFIS) for modelling throughput under production uncertainties. Five significant factors were considered as the main uncer...

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Main Authors:	Amir, Azizi, Amir Yazid, Ali, Loh, Wei Ping
Format:	Article
Language:	English
Published:	IDOSI Publication 2013
Subjects:	TS Manufactures
Online Access:	http://umpir.ump.edu.my/id/eprint/5494/ http://umpir.ump.edu.my/id/eprint/5494/ http://umpir.ump.edu.my/id/eprint/5494/ http://umpir.ump.edu.my/id/eprint/5494/1/Published.pdf

id	ump-5494
recordtype	eprints
spelling	ump-54942018-02-23T02:44:20Z http://umpir.ump.edu.my/id/eprint/5494/ An Adaptive Neuro-Fuzzy Inference System for a Dynamic Production Environment under Uncertainties Amir, Azizi Amir Yazid, Ali Loh, Wei Ping TS Manufactures Throughput modelling evaluates the performance and behaviour of the production systems. This study examined the potential application of Adaptive Neuro-Fuzzy Inference System (ANFIS) for modelling throughput under production uncertainties. Five significant factors were considered as the main uncertainties of production: scrap, setup time, break time, demand and lead time of manufacturing. Observations on the production uncertainties had been performed for 104 weeks in a tile manufacturing industry. The results of ANFIS model had been compared with Multiple Linear Regression (MLR) model. The results showed that ANFIS model was capable of providing adjusted R-squared of 98%, which was higher than the MLR mode IDOSI Publication 2013 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/5494/1/Published.pdf Amir, Azizi and Amir Yazid, Ali and Loh, Wei Ping (2013) An Adaptive Neuro-Fuzzy Inference System for a Dynamic Production Environment under Uncertainties. World Applied Sciences Journal, 25 (3). pp. 428-433. ISSN 1818-4952 http://www.idosi.org/wasj/wasj25(3)2013.htm DOI: 10.5829/idosi.wasj.2013.25.03.63
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TS Manufactures
spellingShingle	TS Manufactures Amir, Azizi Amir Yazid, Ali Loh, Wei Ping An Adaptive Neuro-Fuzzy Inference System for a Dynamic Production Environment under Uncertainties
description	Throughput modelling evaluates the performance and behaviour of the production systems. This study examined the potential application of Adaptive Neuro-Fuzzy Inference System (ANFIS) for modelling throughput under production uncertainties. Five significant factors were considered as the main uncertainties of production: scrap, setup time, break time, demand and lead time of manufacturing. Observations on the production uncertainties had been performed for 104 weeks in a tile manufacturing industry. The results of ANFIS model had been compared with Multiple Linear Regression (MLR) model. The results showed that ANFIS model was capable of providing adjusted R-squared of 98%, which was higher than the MLR mode
format	Article
author	Amir, Azizi Amir Yazid, Ali Loh, Wei Ping
author_facet	Amir, Azizi Amir Yazid, Ali Loh, Wei Ping
author_sort	Amir, Azizi
title	An Adaptive Neuro-Fuzzy Inference System for a Dynamic Production Environment under Uncertainties
title_short	An Adaptive Neuro-Fuzzy Inference System for a Dynamic Production Environment under Uncertainties
title_full	An Adaptive Neuro-Fuzzy Inference System for a Dynamic Production Environment under Uncertainties
title_fullStr	An Adaptive Neuro-Fuzzy Inference System for a Dynamic Production Environment under Uncertainties
title_full_unstemmed	An Adaptive Neuro-Fuzzy Inference System for a Dynamic Production Environment under Uncertainties
title_sort	adaptive neuro-fuzzy inference system for a dynamic production environment under uncertainties
publisher	IDOSI Publication
publishDate	2013
url	http://umpir.ump.edu.my/id/eprint/5494/ http://umpir.ump.edu.my/id/eprint/5494/ http://umpir.ump.edu.my/id/eprint/5494/ http://umpir.ump.edu.my/id/eprint/5494/1/Published.pdf
first_indexed	2023-09-18T22:00:49Z
last_indexed	2023-09-18T22:00:49Z
_version_	1777414385951571968

An Adaptive Neuro-Fuzzy Inference System for a Dynamic Production Environment under Uncertainties

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