Title :
Prediction of manufacturing lead time based on Adaptive Neuro-Fuzzy Inference System (ANFIS)
Author :
Behrouznia, A. ; Azadeh, A. ; Pichka, Kh ; Pazhoheshfar, P. ; Saberi, M.
Author_Institution :
Tafresh Branch, Dept. of Manage., Islamic Azad Univ., Tafresh, Iran
Abstract :
The lead time estimation is significant activity in each corporation that concerns with the breakdown of machines and maintenance. An integrated algorithm for forecasting weekly lead time based on Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed in this study. First, an ANFIS model is illustrated for the lead time forecasting simultaneously. The lowest Mean Absolute Percentage Error (MAPE) value is used to select the best model. In order to illustrate the applicability and superiority of the proposed algorithm, the weekly lead time of Motogen Company in Iran for 70 weeks is used and applied to the proposed algorithm.
Keywords :
fuzzy reasoning; machinery; maintenance engineering; production engineering computing; production management; ANFIS model; Motogen Company; adaptive neuro-fuzzy inference system; lead time estimation; machine breakdown; machine maintenance; manufacturing lead time prediction; mean absolute percentage error value; Adaptation models; Artificial neural networks; Data models; Electric breakdown; Estimation; Production; Training; ANFIS; Lead time; MAPE;
Conference_Titel :
Innovations in Intelligent Systems and Applications (INISTA), 2011 International Symposium on
Conference_Location :
Istanbul
Print_ISBN :
978-1-61284-919-5
DOI :
10.1109/INISTA.2011.5946049
