Premium
Multiobjective Genetic Algorithm Approach to the Economic Statistical Design of Control Charts with an Application to X ¯ bar and S 2 Charts
Author(s) -
Faraz Alireza,
Saniga Erwin
Publication year - 2013
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.1390
Subject(s) - control chart , flexibility (engineering) , heuristic , strengths and weaknesses , adaptability , computer science , statistical process control , set (abstract data type) , optimal design , statistical model , mathematical optimization , control (management) , process (computing) , industrial engineering , engineering , machine learning , statistics , mathematics , artificial intelligence , economics , philosophy , management , epistemology , programming language , operating system
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.