CASE-BASED KNOWLEDGE ACQUISITION FOR SCHEDULE OPTIMIZATION

In recent years, there have been a lot of efforts in solving scheduling problems by using the techniques of artificial intelligence (AI). Through development of a variety of AI-based scheduling systems, it became well known that eliciting effective problem-solving knowledge from human experts is a difficult task, and human schedulers typically lack the knowledge of solving large and complicated scheduling problems in the sophisticated manner. In this paper, our case-based approach, implemented in the system called CABINS, is presented for capturing a human expert's preferential criteria about schedule quality and control knowledge to speed up problem solving. By iterative schedule repair, CABINS improves the quality of sub-optimal schedules, and during the process CABINS utilizes past repair experiences for (1) repair tactic selection and (2) repair result evaluation. It is empirically demonstrated that CABINS can optimize a schedule along objectives captured in its case base and improve the efficiency of optimization process while preserving the quality of a resultant schedule.