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: | , , |
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| Format: | Article |
| Language: | en |
| Published: |
IDOSI Publication
2013
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/5494/1/Published.pdf http://umpir.ump.edu.my/id/eprint/5494/ http://www.idosi.org/wasj/wasj25(3)2013.htm |
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| Summary: | 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
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