α-cut fuzzy control charts for linguistic data

The major contribution of fuzzy set theory is its capability of representing vague data. Fuzzy logic offers a systematic base in dealing with situations that are ambiguous or not well defined. In the literature, there exist some fuzzy control charts developed for linguistic data that are mainly based on membership and probabilistic approaches. In this article, α-cut control charts for attributes are developed. This approach provides the ability of determining the tightness of the inspection by selecting a suitable α-level: The higher α the tighter inspection. The article also presents a numerical example and interprets and compares other results with the approaches developed previously.