Production planning in batch process industries: comparing regression analysis and neural networks

Batch processing becomes more important in the process industries, because of the increasing product variety and the decreasing demand volumes for individual products. In batch process industries it is difficult to predict the completion time, or makespan, of a set of jobs, because jobs interact at the shop floor. In this paper, we compare two different methods for predicting the makespan of job sets in batch process industries. The first method predicts the makespan of a job set by developing regression models, the second method by training neural networks. Both methods use aggregate job-set information.