Affiliations: [a] Department of Industrial Engineering, Shahed University, Tehran, Iran
| [b] Department of Mechanical Engineering, University of Texas at San Antonio, San Antonio, TX, USA
Abstract: Economic-statistical design of control charts has been extensively discussed in the recent literature of Statistical Process Control (SPC). Meanwhile,
X¯
control charts have received more attention due to their simplicity and vast applications. However, due to the effects of multiple factors in real applications, it is difficult to specify accurate values for the economic parameters such as cost and profit, which are associated with the design of
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chart. Hence, It is necessary to enter uncertainty parameters into the control chart design model. In this paper, an economic-statistical design of
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Shewhart control chart with fuzzy parameters has been studied. Two modeling methods have been proposed and then Genetic Algorithm (GA) and Non-dominated Sorting Genetic Algorithm II (NSGA II) are applied to optimize the multi-objective model with the two aggregated and non-aggregated approaches. A numerical example is given for validation of the proposed models and solution algorithms. Finally, some sensitivity analyses are conducted.
Keywords: Statistical Process Control (SPC), fuzzy parameters, economic-statistical design, Shewhart control chart, Genetic algorithm (GA), Non-dominated sorting genetic algorithm II (NSGA II)
