Abstract
A hybrid method that combines human intelligence, an optimization technique (semi-Markov decision model) and an artificial neural network to solve real-time scheduling problems is proposed. The proposed method consists of three phases: data collection, optimization, and generalization. The testbed of this approach is the robot scheduling problem in a circuit board production line where one overhead robot is used to transport jobs through a line of sequential chemical process tanks. Because chemical processes are involved in this production system, any mistiming or misplacing will result in defective jobs. The proposed hybrid system performs better than the human scheduler from whom the models were formulated, both in terms of productivity and quality.
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