Application of data mining for job shop scheduling problem

摘要

In the process of genetic algorithm (GA) for Job shop scheduling problems (JSP), large amounts of data and intermediate information will be produced, which may contain a lot of useful information. Therefore, mining to explore the useful information is necessary. In this paper attribute-oriented induction (AOI) algorithm and dividing hashing and array (DHA) association rule mining algorithm are used to find the useful association rules. The results of them are very similar. But there a few subtle difference. DHA mining results further reflect the relationship between the rules. The application of results shows that it can improve the genetic algorithm initial population fitness effectively.