A DYNAMIC, REALTIME APPROACH TO LONG-DURATION MANUFACTURING SCHEDULE MONITORING AND CONTROL

Planning networks like PERT and CPM do not address all of the planning, scheduling and monitoring problems associated with long-duration, low-volume manufacturing typical in the aerospace industry. Other methods such as MRP and Perti nets are often also limited in their capabilities to analyze such systems. This paper presents a network representation based on the artificial intelligence knowledge representation technique of semantic networks. By explicity representing relationships between operations, resources, milestones, components, schedules and progress, this network approach can isolate conflicts that violate preset system constraints. Steps for creating the network, along with algorithms for network analysis are also presented.