MODELLING PRODUCTION UNCERTAINTIES USING THE ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM

Authors

  • Amir Azizi Universiti Sains Malaysia (USM)
  • Amir Yazid b. Ali Universiti Sains Malaysia (USM)
  • Loh Wei Ping Universiti Sains Malaysia (USM)

DOI:

https://doi.org/10.7166/26-1-560

Keywords:

production throughput, Uncertainty, Neuro-Fuzzy

Abstract

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.

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Published

2015-05-08

How to Cite

Azizi, A., Yazid b. Ali, A., & Wei Ping, L. (2015). MODELLING PRODUCTION UNCERTAINTIES USING THE ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM. The South African Journal of Industrial Engineering, 26(1), 224–234. https://doi.org/10.7166/26-1-560

Issue

Vol. 26 No. 1 (2015)

Section

General Articles

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