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• W2068850172 abstract "Among innovations and improvements that occurred in the past two decades on the techniques and tools used for statistical process control (SPC), adaptive control charts have shown to substantially improve the statistical and/or economical performances. Variable sampling intervals (VSI) control charts are one of the most applied types of the adaptive control charts and have shown to be faster than traditional Shewhart control charts in identifying small changes of concerned quality characteristics. While in the designing procedure of the VSI control charts the data or measurements are assumed independent normal observations, in real situations the validity of these assumptions is under question in many processes. This article develops an economic-statistical design of a VSI X-bar control chart under non-normality and correlation. Since the proposed design consists of a complex nonlinear cost model that cannot be solved using a classical optimization method, a genetic algorithm (GA) is employed to solve it. Moreover, to improve the performances, response surface methodology (RSM) is employed to calibrate GA parameters. The solution procedure, efficiency, and sensitivity analysis of the proposed design are demonstrated through a numerical illustration at the end." @default.
• W2068850172 created "2016-06-24" @default.
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• W2068850172 creator A5052809442 @default.
• W2068850172 creator A5064303268 @default.
• W2068850172 date "2013-10-24" @default.
• W2068850172 modified "2023-09-27" @default.
• W2068850172 title "A Parameter-Tuned Genetic Algorithm for Economic-Statistical Design of Variable Sampling Interval X-Bar Control Charts for Non-Normal Correlated Samples" @default.
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• W2068850172 doi "https://doi.org/10.1080/03610918.2012.732176" @default.
• W2068850172 hasPublicationYear "2013" @default.
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