Computational Intelligence Approach for Process Parameter Settings Using Knowledge Representation

Abstract—This study proposes a fuzzy approach which integrates fuzzy rule sets in a chromosome. To enhance the functionality and capability of the fuzzy set, Genetic Algorithms (GA) technique is incorporated to produce a better and improved fuzzy set which is able to generate the expected result. Past data were selected to create the chromosomes and form the primary population set. This approach capitalizes on the merits of both techniques and offsets the drawbacks of them which may undermine the performance. This research signifies the hybrid approach to identify the optimal criteria for process control in order to achieve the target of the whole operations with an innovative methodology that has not been covered adequately to-date. A case example has been conducted to validate the practicality of the approach and the outcome demonstrated that the proposed approach is able to achieve the results as expected.

Index Terms—Genetic algorithms, demand uncertainty, supply chain management, fuzzy logic model.

Henry C. W. Lau is with School of Business, University of Western Sydney, Australia (e-mail: H.lau@uws.edu.au).

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Cite: Henry C. W. Lau, "Computational Intelligence Approach for Process Parameter Settings Using Knowledge Representation," Lecture Notes on Software Engineering vol. 3, no. 1, pp. 49-52, 2015.