In recent years, there have been a lot of efforts in solving scheduling problems by using the techniques of artificial intelligence. But, through development of a variety of AI-based systems, it became well-known that eliciting effective problem-solving knowledge from human experts is an arduous job, and human schedulers typically lack the knowledge of solving a large and complicated scheduling problem in the sophisticated manner. In this paper our case-based approach, implemented in the system called CABINS, is presented for capturing human expert's preferencial criteria about schedule quality and search 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 scheduling experiences for (1) repair tactic selection and (2) repair result evaluation. In the paper, it was empirically demonstrated that CABINS could improve the efficiency of repair process while preserving the quality of a resultant schedule.
