Abstract
In developing intelligent scheduling systems, it is important to determine an effective method for the acquisition of rules. This paper proposes a method for the acquisition of heuristic scheduling rules from a training set by an inductive learning and investigates its feasibility. We use ID3 learning algorithm for the rule acquisition, which generates a decision tree inductively from a training set. A training set, which plays important role in the inductive learning, is proposed to be generated by interchanging two jobs in a sample schedule in this study. The applicability of the inductive learning and the effectiveness of the generation of the training set are investigated by applying the proposed method to some scheduling problems. The effect of the size of the training set on the rule acquisition is theoretically and numerically analyzed from the entropy view point. Firstly, we apply the method to one machine scheduling problems, of which simple optimum rule is known, and show a possibility of the acquisition of rules by our method. As a result, it is shown that the obtained rules are equivalent to the optimum rules. Secondly, by applying the method to one machine problems of which simple optimum rule is not known and the flow-shop problems, it is shown that effective rules can be acquired. Lastly, we demonstrate the effectiveness of a two phase method to obtain a sample schedule for the second phase acquisition by applying the rules obtained by the first phase acquisition.
