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Data-mining-based dynamic dispatching rule selection mechanism for shop floor control systems using a support vector machine approach

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The purpose of this paper is to develop a data-mining-based dynamic dispatching rule selection mechanism for a shop floor control system to make real-time scheduling decisions. In data mining processes, data transformations (including data normalisation and feature selection) and data mining algorithms greatly influence the predictive accuracy of data mining tasks. Here, the z-scores data normalisation mechanism and genetic-algorithm-based feature selection mechanism are used for data transformation tasks, then support vector machines (SVMs) is applied for the dynamic dispatching rule selection classifier. The simulation experiments demonstrate that the proposed data-mining-based approach is more generalisable than approaches that do not employ a data-mining-based approach, in terms of accurately assigning the best dispatching strategy for the next scheduling period. Moreover, the proposed SVM classifier using the data-mining-based approach yields a better system performance than obtained with a classical SVM-based dynamic dispatching rule selection mechanism and heuristic individual dispatching rules under various performance criteria over a long period.
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Keywords: CIM; FMS control; automated manufacturing systems; data mining; e-manufacturing; manufacturing control systems; neural network applications; semiconductor manufacture; shop floor control

Document Type: Research Article

Affiliations: Department of Information Management, Huafan University, Taipei, Taiwan, R.O.C.

Publication date: January 1, 2009

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