Abstract
The completion time or makespan estimation of a set of jobs in batch process industries is studied; Because jobs interact with each other at the level of shop floor, five interaction variables are defined and an indication of their influence on the makespan is experimentally investigated. A back-propagation network (BPN) model combined with genetic algorithms (GAs) to makespan estimation is proposed. GAs are adopted in the BPN to determine the parameters of BPN and to improve the accuracy of makespan estimation. Previously, there have been no appropriate rules to determine these parameters, 1000 training instances were used for training and evaluating the performance of the model. The study shows that combine GA with NN approach is more effective and accurate in estimating makespan than the BPN model by using trial and error.
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