The Experts below are selected from a list of 189 Experts worldwide ranked by ideXlab platform
Ahmet Sermet Anagün - One of the best experts on this subject based on the ideXlab platform.
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Different methods to fuzzy X¯-R control charts used in Production: Interval type-2 fuzzy set example
Journal of Enterprise Information Management, 2018Co-Authors: Hatice Ercan Teksen, Ahmet Sermet AnagünAbstract:The control charts are used in many Production areas because they give an idea about the quality characteristic(s) of a product. The control limits are calculated and the data are examined whether the quality characteristic(s) is/are within these limits. At this point, it may be confusing to comment, especially if it is slightly below or above the limit values. In order to overcome this situation, it is suitable to use fuzzy numbers instead of crisp numbers. The purpose of this paper is to demonstrate how to create control limits of X ¯ -R control charts for a specified data set of Interval type-2 fuzzy sets.,There are methods in the literature, such as defuzzification, distance, ranking and likelihood, which may be applicable for Interval type-2 fuzzy set. This study is the first that these methods are adapted to the X ¯ -R control charts. This methodology enables Interval type-2 fuzzy sets to be used in X ¯ -R control charts.,It is demonstrated that the methods – such as defuzzification, distance, ranking and likelihood for Interval type-2 fuzzy sets – could be applied to the X ¯ -R control charts. The fuzzy control charts created using the methods provide similar results in terms of in/out control situations. On the other hand, the sample points depicted on charts show similar pattern, even though the calculations are different based on their own structures. Finally, the control charts obtained with Interval type-2 fuzzy sets and the control charts obtained with crisp numbers are compared.,Based on the related literature, research works on Interval type-2 fuzzy control charts seem to be very limited. This study shows the applicability of different Interval type-2 fuzzy methods on X ¯ -R control charts. For the future study, different Interval type-2 fuzzy methods may be considered for X ¯ -R control charts.,The unique contribution of this research to the relevant literature is that Interval type-2 fuzzy numbers for quantitative control charts, such as X ¯ -R control charts, is used for the first time in this context. Since the research is the first adaptation of Interval type-2 fuzzy sets on X ¯ -R control charts, the authors believe that this study will lead and encourage the people who work on this topic.
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different methods to fuzzy x r control charts used in Production Interval type 2 fuzzy set example
Journal of Enterprise Information Management, 2018Co-Authors: Hatice Ercan Teksen, Ahmet Sermet AnagünAbstract:The control charts are used in many Production areas because they give an idea about the quality characteristic(s) of a product. The control limits are calculated and the data are examined whether the quality characteristic(s) is/are within these limits. At this point, it may be confusing to comment, especially if it is slightly below or above the limit values. In order to overcome this situation, it is suitable to use fuzzy numbers instead of crisp numbers. The purpose of this paper is to demonstrate how to create control limits of X ¯ -R control charts for a specified data set of Interval type-2 fuzzy sets.,There are methods in the literature, such as defuzzification, distance, ranking and likelihood, which may be applicable for Interval type-2 fuzzy set. This study is the first that these methods are adapted to the X ¯ -R control charts. This methodology enables Interval type-2 fuzzy sets to be used in X ¯ -R control charts.,It is demonstrated that the methods – such as defuzzification, distance, ranking and likelihood for Interval type-2 fuzzy sets – could be applied to the X ¯ -R control charts. The fuzzy control charts created using the methods provide similar results in terms of in/out control situations. On the other hand, the sample points depicted on charts show similar pattern, even though the calculations are different based on their own structures. Finally, the control charts obtained with Interval type-2 fuzzy sets and the control charts obtained with crisp numbers are compared.,Based on the related literature, research works on Interval type-2 fuzzy control charts seem to be very limited. This study shows the applicability of different Interval type-2 fuzzy methods on X ¯ -R control charts. For the future study, different Interval type-2 fuzzy methods may be considered for X ¯ -R control charts.,The unique contribution of this research to the relevant literature is that Interval type-2 fuzzy numbers for quantitative control charts, such as X ¯ -R control charts, is used for the first time in this context. Since the research is the first adaptation of Interval type-2 fuzzy sets on X ¯ -R control charts, the authors believe that this study will lead and encourage the people who work on this topic.
Hatice Ercan Teksen - One of the best experts on this subject based on the ideXlab platform.
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Different methods to fuzzy X¯-R control charts used in Production: Interval type-2 fuzzy set example
Journal of Enterprise Information Management, 2018Co-Authors: Hatice Ercan Teksen, Ahmet Sermet AnagünAbstract:The control charts are used in many Production areas because they give an idea about the quality characteristic(s) of a product. The control limits are calculated and the data are examined whether the quality characteristic(s) is/are within these limits. At this point, it may be confusing to comment, especially if it is slightly below or above the limit values. In order to overcome this situation, it is suitable to use fuzzy numbers instead of crisp numbers. The purpose of this paper is to demonstrate how to create control limits of X ¯ -R control charts for a specified data set of Interval type-2 fuzzy sets.,There are methods in the literature, such as defuzzification, distance, ranking and likelihood, which may be applicable for Interval type-2 fuzzy set. This study is the first that these methods are adapted to the X ¯ -R control charts. This methodology enables Interval type-2 fuzzy sets to be used in X ¯ -R control charts.,It is demonstrated that the methods – such as defuzzification, distance, ranking and likelihood for Interval type-2 fuzzy sets – could be applied to the X ¯ -R control charts. The fuzzy control charts created using the methods provide similar results in terms of in/out control situations. On the other hand, the sample points depicted on charts show similar pattern, even though the calculations are different based on their own structures. Finally, the control charts obtained with Interval type-2 fuzzy sets and the control charts obtained with crisp numbers are compared.,Based on the related literature, research works on Interval type-2 fuzzy control charts seem to be very limited. This study shows the applicability of different Interval type-2 fuzzy methods on X ¯ -R control charts. For the future study, different Interval type-2 fuzzy methods may be considered for X ¯ -R control charts.,The unique contribution of this research to the relevant literature is that Interval type-2 fuzzy numbers for quantitative control charts, such as X ¯ -R control charts, is used for the first time in this context. Since the research is the first adaptation of Interval type-2 fuzzy sets on X ¯ -R control charts, the authors believe that this study will lead and encourage the people who work on this topic.
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different methods to fuzzy x r control charts used in Production Interval type 2 fuzzy set example
Journal of Enterprise Information Management, 2018Co-Authors: Hatice Ercan Teksen, Ahmet Sermet AnagünAbstract:The control charts are used in many Production areas because they give an idea about the quality characteristic(s) of a product. The control limits are calculated and the data are examined whether the quality characteristic(s) is/are within these limits. At this point, it may be confusing to comment, especially if it is slightly below or above the limit values. In order to overcome this situation, it is suitable to use fuzzy numbers instead of crisp numbers. The purpose of this paper is to demonstrate how to create control limits of X ¯ -R control charts for a specified data set of Interval type-2 fuzzy sets.,There are methods in the literature, such as defuzzification, distance, ranking and likelihood, which may be applicable for Interval type-2 fuzzy set. This study is the first that these methods are adapted to the X ¯ -R control charts. This methodology enables Interval type-2 fuzzy sets to be used in X ¯ -R control charts.,It is demonstrated that the methods – such as defuzzification, distance, ranking and likelihood for Interval type-2 fuzzy sets – could be applied to the X ¯ -R control charts. The fuzzy control charts created using the methods provide similar results in terms of in/out control situations. On the other hand, the sample points depicted on charts show similar pattern, even though the calculations are different based on their own structures. Finally, the control charts obtained with Interval type-2 fuzzy sets and the control charts obtained with crisp numbers are compared.,Based on the related literature, research works on Interval type-2 fuzzy control charts seem to be very limited. This study shows the applicability of different Interval type-2 fuzzy methods on X ¯ -R control charts. For the future study, different Interval type-2 fuzzy methods may be considered for X ¯ -R control charts.,The unique contribution of this research to the relevant literature is that Interval type-2 fuzzy numbers for quantitative control charts, such as X ¯ -R control charts, is used for the first time in this context. Since the research is the first adaptation of Interval type-2 fuzzy sets on X ¯ -R control charts, the authors believe that this study will lead and encourage the people who work on this topic.
Yuehwern Yih - One of the best experts on this subject based on the ideXlab platform.
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Development of a real-time multi-objective scheduler for a semiconductor fabrication system
International Journal of Production Research, 2003Co-Authors: Hyeung-sik Min, Yuehwern YihAbstract:Semiconductor wafer fabrication involves possibly one of the most complex manufacturing processes ever used. This causes a number of decision problems. A successful system control strategy would assign appropriate decision rules for decision variables. Therefore, the goal of this study is to develop a scheduler for the selection of decision rules for decision variables in order to obtain the desired performance measures given by a user at the end of a certain Production Interval. In this proposed methodology, a system control strategy based on a simulation technique and a competitive neural network is suggested. A simulation experiment was conducted to collect the data containing the relationship between the change of decision rule set and current system status and the performance measures in the dynamic nature of semiconductor manufacturing fabrication. Then, a competitive neural network was applied to obtain the scheduling knowledge from the collected data. The results of the study indicate that applyin...
Anis Chelbi - One of the best experts on this subject based on the ideXlab platform.
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Optimal Production plan for a multi-products manufacturing system with Production rate dependent failure rate
International Journal of Production Research, 2012Co-Authors: Mohamed Dahane, Nidhal Rezg, Anis ChelbiAbstract:This study deals with the problem of dependence between Production and failure rates in the context of a multi-product manufacturing system. It provides an answer about how to produce (i.e. the Production rates) and what to produce (i.e. which product) over a finite horizon of H periods of equal length. We consider a single randomly failing and repairable manufacturing system producing two products Pa and Pb . The product Pa is produced to supply the strategic demand d(k) of the principal customer via a buffer stock S over k periods (k = 1, 2, … , H). The second product Pb is produced to meet a secondary but very profitable demand. It is produced during a given Interval at the end of each period k. We develop a genetic algorithm to determine simultaneously the optimal Production rate of the first product during each period k and the optimal duration of the Production Interval of the second product, maximising the total expected profit.
Jesús Pozo - One of the best experts on this subject based on the ideXlab platform.
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Life history and Production of Caenis luctuosa (Burmeister) (Ephemeroptera, Caenidae) in two nearby reaches along a small stream
Hydrobiologia, 2001Co-Authors: Juan M. Gonzalez, Ana Basaguren, Jesús PozoAbstract:Population dynamics and Production of C. luctuosa were compared in two reaches of the Aguera stream (northern Spain). This species showed univoltine winter life history in both sites. However, the start of the recruitment period, and the cohort Production Interval differed in 1 month between reaches. Secondary Production of C. luctuosa ranged from 76 mg m −2 year −1 (upper site) to 93 mg m −2 year −1 (lower site). Although annual Production seemed to be mainly influenced by the biomass found at each site, changes in life history may have also been important. The need to have accurate information about life history of the analysed species at the study sites when assessing secondary Production is highlighted.
