توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱An Adaptive Neuro–Fuzzy Inference System for a Dynamic Production Environment under Uncertainties
نویسنده(ها): ، ،
اطلاعات انتشار: World Applied Sciences Journal، بيست و پنجم،شماره۳، ۲۰۱۳، سال
تعداد صفحات: ۶
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 model.
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