A Self-adaptive Knowledge Based Dynamic Scheduling Decision Mechanism

Through the interrelated concept of the shop floor production, this paper constructs a scheduling decision system based on the Contract Net Protocol, 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 our dynamic scheduling decision system. Based on the algorithmic analysis, we demonstrate its convergence and credibility. Applying this algorithm, the system will explore the more useful scheduling decisions in the changeful environment continuously, and update knowledge of the system, thus it will avoid the subjectivity and invariance of the expert knowledge. As a result, the knowledge-based dynamic scheduling decision system will be more flexible, and adjust itself according to the dynamic environment continuously