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A dynamic adaptive system for economic design of multiple control charts

Chen‐Fang Tsai (Tamsui Oxford University College, ROC)
Chris Bowerman (University of Sunderland, Sunderland, UK)
John Tait (University of Sunderland, Sunderland, UK)

Integrated Manufacturing Systems

ISSN: 0957-6061

Article publication date: 1 July 2000

Abstract

Much research has recently been conducted into the use of models for the economic design of multiple control charts (EDCC). Control chart models generally assume that most process variables are constant and only a limited number of the major variables are varied to reach a local optimum. In the economic design of multiple control charts (EDMCC), multiple control charts are used to analyse many manufacturing process variables simultaneously, in order to produce an optimal design for process control. However, the large number of variables often makes it difficult to solve this optimisation problem manually. This research explores the proposition that EDMCC can be optimised by using a novel genetic algorithm which dynamically adjusts the genetic algorithm’s (GA) operator and parameter settings during operation to ensure optimum effectiveness. This method involves refining the chromosome structure and using orthogonal arrays with fuzzy reasoning to reduce the search space.

Keywords

Citation

Tsai, C., Bowerman, C. and Tait, J. (2000), "A dynamic adaptive system for economic design of multiple control charts", Integrated Manufacturing Systems, Vol. 11 No. 4, pp. 277-285. https://doi.org/10.1108/09576060010326401

Publisher

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MCB UP Ltd

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