Abstract
It is difficult to predict the completion time of a set of jobs in batch process industries, because jobs interact at the shop floor. Five interaction variables are defined and an indication of their influence on the completion time is experimentally investigated, and there have been no appropriate rules to determine these variables. This paper combines the back-propagation network model and genetic algorithms to completion time prediction. Genetic algorithms were adopted in the back-propagation network to determine the back-propagation network's parameters and to improve the accuracy of completion time prediction. Tests on newly generated job sets showed that hybrid method of back-propagation network and genetic algorithms was more effective and accurate in predicting the completion time than the back-propagation network model using trial and error.
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