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.