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A dynamic adaptive system for economic design of multiple control charts

A dynamic adaptive system for economic design of multiple control charts 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Integrated Manufacturing Systems Emerald Publishing

A dynamic adaptive system for economic design of multiple control charts

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References (30)

Publisher
Emerald Publishing
Copyright
Copyright © 2000 MCB UP Ltd. All rights reserved.
ISSN
0957-6061
DOI
10.1108/09576060010326401
Publisher site
See Article on Publisher Site

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.

Journal

Integrated Manufacturing SystemsEmerald Publishing

Published: Jul 1, 2000

Keywords: Fuzzy logics; Control charts; Modelling

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