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Abstract
Control charts are one of the most important tools in statistical process control that lead to improve quality processes and ensure required quality levels. In traditional control charts, all data should be exactly known, whereas there are many quality characteristics that cannot be expressed in numerical scale, such as characteristics for appearance, softness, and color. Fuzzy sets theory is powerful mathematical approach to analyze uncertainty, ambiguous and incomplete that can linguistically define data in these situations. Fuzzy control charts have been extended by converting the fuzzy sets associated with linguistic or uncertain values into scalars regarded as representative values. In this paper, we develop a new fuzzy control chart for monitoring attribute quality characteristics based on $\alpha-$level fuzzy midrange approach. Finally, the performance and comparative results of the proposed fuzzy control chart is measured in terms of average run length (ARL) by Mont Carlo simulation.
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