A neuro-genetic algorithm for parallel machine scheduling to determine the number of machines and priority scheduling rules
Yildirim, Mehmet Bayram
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In this paper, we propose a neuro-genetic artificial neural network framework to achieve certain targeted productivity measures/ performance values in a flow shop with parallel processors (resources) at each stage. The performance measures that we consider are flow time, number of tardy jobs, total tardiness and machine utilizations. In order to achieve these goals, the management has to make decisions on the availability of resources, in our setting, the number of identical machines in each work station and the dispatching rule to be utilized in the shop floor to achieve performance values as close as to the targeted ones.
This is the author's copy of the conference paper published in the proceedings of the International Conference on Productivity and Quality Research, 10th Commemorative International, Miami, Fl.