Multiobjective genetic algorithm approach to the economic statistical design of control charts with an application to X ̄ bar and S 2 charts

Alireza Faraz

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)407-415
Number of pages9
JournalQuality and Reliability Engineering International
Volume29
Issue number3
DOIs
Publication statusPublished - Apr 2013

Keywords

  • control charts
  • economic statistical design
  • genetic algorithm
  • multiobjective optimization

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