Knowledge Update in a Knowledge-Based Dynamic Scheduling Decision System
Through the interrelated concept of the job shop production, this paper constructs a dynamic scheduling decision system based on knowledge, and gives five attributes of resource agent and corresponding task, time, cost, quality, load and priority. Using the fuzzy set and rough set, the classified knowledge of the attribute is generated, and is used as the states criteria in the Q-learning. To initialize Q value of the decision attribute, we collect the knowledge from experts. The Q-learning algorithm and initial parameter values are presented in knowledge based scheduling decision model. By the algorithmic analysis, we demonstrate its convergence and credibility. Applying this algorithm, the system will update the knowledge itself continuously, and it will be more intelligent in the changeful environment, also it will avoid the subjectivity and invariance of the expert knowledge.
KeywordsReinforcement Learning Shop Floor Schedule Decision Resource Agent Schedule Rule
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