Approach to Job-Shop Scheduling Problem Using Rule Extraction Neural Network Model
Keywords:
focuses, Genetic algorithms
Abstract
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 3x3 scheduling instances. The capability of the rule-based scheduler in providing near optimal solutions is also discussed in this thesis.
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Published
2011-03-15
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Copyright (c) 2011 Authors and Global Journals Private Limited
This work is licensed under a Creative Commons Attribution 4.0 International License.