ITERATIVE LEARNING SCHEDULING BASED ON ADAPTIVE POLICY

Adaptive scheduling is a kind of qualitative control scheme based on the state-performance feedback. Adaptive scheduling knowledge maps the states to a scheduling rule. It is problem specific; that is, it is closely related with the layout of manufacturing systems, the contents of production tasks and the scheduling objective functions. When used for scheduling a production task, the adaptive scheduling knowledge should be modified accordingly so as to be suitable for the task and consequently to obtain higher performance. This paper presents an iterative learning scheme that is used to refine the adaptive scheduling knowledge according to the problem scheduled. The scheduling objective, which reflects the performances of interest, is optimized during the iterative learning procedure. Experimental results demonstrate efficiency and effectiveness of the iterative learning scheduling.