UMP Institutional Repository

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

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

[img] PDF
Published.pdf - Published Version
Restricted to Repository staff only

Download (306kB) | Request a copy

Abstract

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

Item Type: Article
Subjects: T Technology > TS Manufactures
Faculty/Division: Faculty of Manufacturing Engineering
Depositing User: Dr. Amir Azizi
Date Deposited: 09 Apr 2014 02:11
Last Modified: 23 Feb 2018 02:44
URI: http://umpir.ump.edu.my/id/eprint/5494
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item