Approach to Job-Shop Scheduling Problem Using Rule Extraction Neural Network Model
This thesis focuses on the development of a rule-based scheduler, based on production rules derived from an artificial neural network performing job shop scheduling. This study constructs a hybrid intelligent model utilizing genetic algorithms for optimization and neural networks as learning tools. Genetic algorithms are used for obtaining optimal schedules and the neural network is trained on these schedules. Knowledge is extracted from the trained network. The performance of this extracted rule set is analyzed in scheduling a test set of 3×3 scheduling instances. The capability of the rule-based scheduler in providing near optimal solutions is also discussed in this thesis.
