1997 Volume 10 Issue 9 Pages 463-469
In developing intelligent scheduling systems, it is important to develop an effective method for acquisition of decision rules. This paper extends the authors' previous work on single machine scheduling problems to flow shop scheduling problems and describes a method for rule acquisition by generating sample schedules and analyzing their properties. For rule acquisition, C4.5 learning algorithm is used and training cases are generated by interchanging two jobs in a sample schedule. Job attributes and location properties are used as the basic information for rule acquisition. Finally, we investigate the effectiveness of the proposed method and demonstrate the applicability of the obtained rules by computational experiments
