Research Initiation Award: Automatic Precedence Knowledge Acquisition and Design of a Real-Time Intelligent Assembly Scheduler with Neural Computing

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The objective of this research is to develop a real-time intelligent assembly system which integrates the operations from the assembly design process to the optimal assembly schedule generation. The goals of the research are: (1) obtain the precedence knowledge of the assembled object automatically either from a rule-based system, or from neural network learning schemes; (2) find the mapping between the precedence knowledge and investigated neural networks; (3) generate the optimal assembly schedule based on the mapping between the precedence knowledge and the network and algorithms; and (4) implement a systematic intelligent assembly system which integrates the design process and the generation of the optimal assembly schedule. The system is applicable to automobile assembly, electronic circuit board assembly, industrial flexible manufacturing and other fields related to the precedence-constraint planning and scheduling problems.