Abstract

Methods are developed on a digital computer for performing work order scheduling activity in a dynamic factory floor environment, in a manner which enables scheduling heuristic knowledge from a scheduler to be encoded through an adaptive learning process, thus eliminating the need to define these rules explicitly. A sequential assignment paradigm incrementally builds up a final schedule from a partial schedule, assigning each work order to appropriate resources in turns, taking advantage of the parallel processing capability of neural networks by selecting the most appropriate resource combination (i.e. schedule generation) for each work order under simultaneous interaction of multiple scheduling constraints.

Other References

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