Abstract
Intelligent production scheduling has been viewed as a problem-solving task that involves the generation of a suitable schedule among different ones using learning techniques. This work presents a unified model for a class of intelligent machines, suitable for flexible manufacturing systems, in which each alternative production plan comprises a fixed number of sequential steps, and the task is to select the optimal plan as more experience is obtained during the operation of the system. This model, based on the theory of hierarchically intelligent control systems developed by Saridis (1995), combines the powerful high-level decision making with advanced mathematical modeling and synthesis techniques of system theory and methods of dealing with imprecise or incomplete environment information.
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