Title :
Artificial neural networks for predicting the optimal number of kanbans in a JIT manufacturing environment
Author :
Narayan, Sridhar ; Wray, Barry A. ; Mathieu, Richard G.
Author_Institution :
Dept. of Comput. Sci., North Carolina Univ., Wilmington, NC, USA
Abstract :
Current techniques for predicting the number of kanbans needed at a workcenter typically use only efficient factory data to develop a predictive model that maps relationships between inputs (shop operating conditions) and a desired output (number of kanbans). The paper presents a methodology that uses autoassociative neural networks to determine if a proposed number of kanbans will result in a starved, efficient, or saturated factory, based on a given set of factory conditions
Keywords :
neural nets; production engineering computing; JIT manufacturing environment; autoassociative neural networks; efficient factory; kanbans; predictive model; saturated factory; shop operating conditions; starved factory; workcenter; Artificial neural networks; Computer aided manufacturing; Computer science; Intelligent networks; Manufacturing processes; Neural networks; Predictive models; Production facilities; Pulp manufacturing; Virtual manufacturing;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.836216
