Reference : Multiobjective Genetic Algorithm Approach to the Economic Statistical Design of Contr...
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Mathematics
Multiobjective Genetic Algorithm Approach to the Economic Statistical Design of Control Charts with an application to Xbar and S2 charts
[en] MOESD joint Xbar & S2
Faraz, Alireza mailto [Université de Liège - ULg > HEC-Ecole de gestion de l'ULg : UER > Statistique appliquée à la gestion et à l'économie >]
Saniga, Erwin mailto [Department of Business Administration, University of Delaware, Newark, Delaware 19716, USA > > > >]
Quality and Reliability Engineering International
John Wiley & Sons
Yes (verified by ORBi)
[en] Multiobjective Optimization; Genetic Algorithm; Economic Statistical Design; Control Charts
[en] Control charts are the primary tools of statistical process control. These charts may be designed by using a simple rule suggested by Shewhart, by 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 above is their lack of flexibility and adaptability, a primary objective of practical mathematical models. In this paper, we explore multi objective models as an alternative for the methods listed above. 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 multi objective designs to economic designs, statistical designs, economic statistical designs and heuristic designs.
Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS
Researchers ; Professionals ; Students

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