Multiobjective Genetic Algorithm Approach to the Economic Statistical Design of Control Charts with an Application to bar and S2 Charts |
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Authors: | Alireza Faraz Erwin Saniga |
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Affiliation: | 1. Centre for Quantitative Methods and Operations Management, HEC Management School, University of Liège, , Liège, 4000 Liege, Belgium;2. Department of Business Administration Department, University of Delaware, , Newark, DE, 19716 USA |
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Abstract: | Control charts are the primary tools of statistical process control. These charts may be designed by using a simple rule suggested by Shewhart, a statistical criterion, an economic criterion, or a joint economic statistical criterion. Each method has its strengths and weaknesses. One weakness of the methods of design listed is their lack of flexibility and adaptability, a primary objective of practical mathematical models. In this article, we explore multiobjective models as an alternative for the methods listed. These provide a set of optimal solutions rather than a single optimal solution and thus allow the user to tailor their solution to the temporal imperative of a specific industrial situation. We present a solution to a well‐known industrial problem and compare optimal multiobjective designs with economic designs, statistical designs, economic statistical designs, and heuristic designs. Copyright © 2012 John Wiley & Sons, Ltd. |
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Keywords: | multiobjective optimization genetic algorithm economic statistical design control charts |
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