This research investigated applying Artificial Intelligence (AI) method to develop a scheduling and sequencing system for parallel processors, subject to preference, sequencing, and buffer inventory constraints. Specifically, hierarchical planning and, constraint-directed search were used to develop prototype scheduling system for a case study problem. This research also investigated dividing the scheduling problem into sub-periods to allow parallel scheduling and efficient handling of time-dependent, constraints. The prototype system uses, problem-constraints to define sub-period boundaries, and determine which processors and jobs to include in the sub-period problems. It then solves the sub-period schedules in sequence. The prototype system was tested using operational data from the case study and compared to schedules created by the case study scheduler. The prototype system produced schedules very similar to the human scheduler, and relaxed constraints only slightly more than the scheduler in searching for solutions. The success of the prototype system demonstrated: 1) the effectiveness of hierarchical planning and constraint-directed search as methods for developing scheduling systems for parallel processors; 2) that constraint satisfaction, as opposed to solving an objective function, is a useful alternative method for modeling scheduling problems; and 3) dividing the scheduling problem into sub-period problems reduces the size of the search space- encountered in parallel scheduling while allowing fulfillment of time dependent constraints. / Master of Science