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, lo w-volume manufacturing typical in the aerospace industry. Other method s such as MRP and Perti nets are often also limited in their capabilit ies to analyze such systems. This paper presents a network representat ion based on the artificial intelligence knowledge representation tech nique of semantic networks. By explicity representing relationships be tween operations, resources, milestones, components, schedules and pro gress, this network approach can isolate conflicts that violate preset system constraints. Steps for creating the network, along with algori thms for network analysis are also presented.