Discovering Dispathcing Rules for Job Shop Schdeuling Using Data Mining

Abstract

This paper introduces a methodology for generating scheduling rules using data mining approach to discover the dispatching sequence by applying learning algorithm directly to job shop scheduling. Job-shop scheduling is one of the well-known hardest combinatorial optimization problems. This paper considers the problem of finding schedule for a job shop to minimize the makespan using Decision Tree algorithm. This approach involves preprocessing of scheduling data into an appropriate data file, discovering the key scheduling concepts and representing of the data mining results in way that enables its use for job scheduling. In decision tree based approach, the attribute selection greatly influences the predictive accuracy and hence this approach also considers creation of additional attributes. The proposed approach is compared with literature and work is complement to the traditional methods.