Fuzzy MaxGWMA chart for identifying abnormal variations of on-line manufacturing processes with imprecise information

Methods in Ranking Fuzzy Numbers: A Unified Index and Comparative Reviews

“…Their subbranches have also been established; for example, fuzzy differential equations [6][7][8][9][10][11][12][13][14] and fuzzy integrodifferential equations [15][16][17][18][19][20][21][22] are of fuzzy mathematics while fuzzy-number ranking, the focus of this paper, is of fuzzy decision-making. Specifically, based on its feasible mathematical capacity for representing the imprecise information in practice, we have observed many successful cases spreading in disparate disciplines, such as robot selection [23], supplier selection [24], logistics center allocation [25], facility location determination [26], choosing mining methods [27], manufacturing process monitoring [1,2,[28][29][30][31], cutting force prediction [32], firm-environmental knowledge management [33,34], green supply-chain operation [35], and weapon procurement decision [36]. Apparently, to find their best alternative, those decisive problems are evaluated under resource constraints and with to some extent linguistic preference of multiattribute, which is realized from users' perspectives, as well as subjective quantification of multiple characteristics, which is assessed from decision-makers [2,3,[37][38][39].…”

“…It has been well recognized that uncertainty inevitably exists in several real-world phenomena due to the inherent errors or impreciseness of measurement tools, methods, and uncontrollable conditions [1,2]. In managing the uncertainty and vagueness, the fuzzy set theory has been widely considered as a powerful tool [3,4].…”

The Fuzzy u-Chart for Sustainable Manufacturing in the Vietnam Textile Dyeing Industry

“…In such industries, the quality of final products practically depends on the subjective perception, knowledge, mood and behavior of the inspectors, also known as quality controllers or assurers who check individual parts, semi-products and final products at different stages in the processes to make sure that their products meet certain specifications set by customers [41]. Consequently, with the manual inspection of random samples, certain limitations in terms of inaccuracy, inconsistency and inefficiency, are obviously inevitable; hence, the recorded data are considered fuzzy [4,41,44]. To have more objective evaluation, automatic inspection systems have been preferably installed despite of their high cost [42].…”

“…However, the natural limitations inherited in practical applications have dampened this possibility. For example, the inevitability of gague errors existing in a measurement system [4,5] and in data collection processes where the human subjectivity arises from the decision-makers' vast variety of intelligence perceptions and experiences all make accumulated data imprecise [6][7][8][9]. Moreover, for the monitoring and controlling of online manufacturing processes, the traditional control charts carrying a binary classification of the process condition, namely in control and out of control,"have failed to effectively adapt to this fuzzy domain.…”

“…Moreover, for the monitoring and controlling of online manufacturing processes, the traditional control charts carrying a binary classification of the process condition, namely in control and out of control,"have failed to effectively adapt to this fuzzy domain. The extensions of traditional control charts, named " fuzzy control charts", become mandated [7,[9][10][11][12][13][14] Over the past decade, worldwide scholars have paid a special interest in proposing and constructing a number of fuzzy control charts; examples include Senturk and Erginel [9] with fuzzy X − R and X − S control charts, Shu and Wu [14] with fuzzy x and R control charts, Nguyen et al [15] with fuzzy x and s control charts, Shu et al [4] with fuzzy MaxGWMA control chart, and Morabia et al [16] with fuzzy x control chart to deal with multiple objective decision-making problems. The core issue of the contributions above is centered on the fuzzy-variable control charts.…”