Genetics-based machine learning approach to production scheduling-a case of in-tree type precedence relation

This paper introduces a method of generating and selecting rules for adjusting the priorities of jobs by using genetics-based machine learning (GBML) techniques. In applying the GBML, the authors use the Pitts approach, where the set of rules (rule-set) are represented symbolically as an individual of genetic algorithms, and the fitness of an individual is calculated based on the makespan of the schedule generated by using the rule-set. They actually carried out computational experiments for several problems, which indicate that the method of applying the GBML is effective for finding good rule-sets.