An application of hierarchical planning and constraint-directed search to scheduling parallel processors

This research investigated the applicability of Artificial Intelligence methods to the problem of scheduling and sequencing parallel processors subject to preference, sequencing, and buffer inventory constraints. Specifically, hierarchical planning and constraint-directed search were used to develop a prototype scheduling system for a case study problem. The prototype´s planning structure included logic for dividing the scheduling period into sub-periods to allow parallel scheduling and handling of time-dependent constraints. The prototype system was tested using operational data from the case study and compared to schedules created by the human scheduler. The prototype system produced schedules very similar to the human scheduler, demonstrating the effectiveness of these methods in developing scheduling systems for parallel processors.