Fuzzy Process Control And Development Of Some Models For Fuzzy Control Charts

Tez (Doktora) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2006Thesis (PhD) -- İstanbul Technical University, Institute of Science and Technology, 2006Bu çalışmada, bulanık kümeler teorisi kullanılarak belirsizlik içeren dilsel verilerin kontrol diyagramlarına yeni yaklaşımlar geliştirilmiştir. Belirsizlik içeren dilsel veriler, bulanık sayılarla ifade edilmiştir. Dilsel veriler için bulanık kontrol diyagramları α-kesim yaklaşımı kullanılarak geliştirilmiş ve bu suretle muayene sıklığı tanımlanmıştır. Bulanık kontrol diyagramlarının oluşturulmasında, bulanık verilerin taşıdığı bilgilerin kaybolmasını önlemek amacıyla “Direkt Bulanık Yaklaşım” geliştirilmiştir. Bulanık verilerin kontrol diyagramındaki normal olmayan davranış testleri için bulanık bir yaklaşım geliştirilmiştir. Önerilen yaklaşımların pratik kullanımlarının yansıtılması açısından gerçek verilere dayalı nümerik örnekler sunulmuştur.In this study, process control charts under linguistic, vague, and uncertain data are developed in the light of the Fuzzy Set Theory. Linguistic or uncertain data are represented by the use of fuzzy numbers. Fuzzy control charts for the linguistic data are proposed and integrated with the α-cut approach of fuzzy sets in order to set the degree of tightness of the inspection. A new approach called direct fuzzy approach to fuzzy control charts is modeled in order to prevent the loss of information of the fuzzy data during the construction of control charts. Finally, fuzzy unnatural pattern analyses are developed to monitor the abnormal patterns of the fuzzy data on the control charts. Numerical examples using the data of a real case are also given to highlight the practical usage of the proposed approaches.DoktoraPh

Fuzzy Modeling of Client Preference in Data-Rich Marketing Environments

Advances in computational methods have led, in the world of financial services, to huge databases of client and market information. In the past decade, various computational intelligence (CI) techniques have been applied in mining this data for obtaining knowledge and in-depth information about the clients and the markets. This paper discusses the application of fuzzy clustering in target selection from large databases for direct marketing (DM) purposes. Actual data from the campaigns of a large financial services provider are used as a test case. The results obtained with the fuzzy clustering approach are compared with those resulting from the current practice of using statistical tools for target selection.fuzzy clustering;direct marketing;client segmentation;fuzzy systems

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The purpose of this study is to present a new approach for fuzzy control charts. The procedure is based on the fundamentals of Shewhart control charts and the fuzzy theory. The proposed approach is developed in such a way that the approach can be applied in a wide variety of processes. The main characteristics of the proposed approach are: The type of the fuzzy control charts are not restricted for variables or attributes, and the approach can be easily modified for different processes and types of fuzzy numbers with the evaluation or judgment of decision maker(s). With the aim of presenting the approach procedure in details, the approach is designed for fuzzy c quality control chart and an example of the chart is explained. Moreover, the performance of the fuzzy c chart is investigated and compared with the Shewhart c chart. The results of simulations show that the proposed approach ha

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The construction industry project is more subjective and risky compared with the others industries because of the unique characteristics of construction activities such as poor working condition, the significant frequency of accidents and the occupational risky situation. Risk analysis and management on the project sites is the first key to achieve adequate level of security. However, the modern construction and the new sophisticated design have shown a significant obstacles and uncertainties to complete the project safely; thereby it is inevitable to search a new approach to deal with uncertainties. The ability of a fuzzy system to deliver its reasoning process is presented to have absolute result within the field of risk analysis. As well as, fuzzy set theory is mainly subjective and associated to deal with inexact and vague information in construction projects. This paper describes the stages of the fuzzy risk analysis model which is developed to assess the risks related with construction projects and their uncertainties based on evaluations of cost, time and quality. Ultimately, using this model we can prioritize and rank all risk factors cited in the construction project; besides that we can easily manage them in the best appropriate way

Fuzzy short-run control charts

Statistical control charts are useful tools in monitoring the state of a manufacturing process. Control charts are used to plot process data and compare it to the limits set for the process. Points plotting outside these limits indicate an out-of-control condition. Standard control charting procedures, however, are limited in that they cannot take into account the case when data is of a fuzzy nature. Another limitation of standard charting methods is when the data produced by the process is short-run data. Often, the situation where the data is short-run occurs in conjunction with data that is considered fuzzy. This paper dicusses the development of a fuzzy control chartting technique, called short-Run α-cut p Control Chart, to account for fuzzy data in a short-run situation. The developed chart parameters accounted for the fuzzy nature of the data in a short-run situation. The parameters were validated by comparing the false alarm rates for various combinations of subgroup numbers (m) and subgroup sizes (n). It was shown that for every combination of m and n, the Short-Run α-cut p Control Chart limits produced a lower false alarm rate than that of the standard fuzzy α-cut control chart.Peer Reviewe

An Intelligent Approach to Prioritize Logistics Requirements in Food Industry

The aim of this study is to analyze dairy food industry and to determine the priorities of important logistics requirements (LR) based on customer requirements as a part of a supply management system. For product or service development, quality function deployment (QFD) is a useful approach to maximize customer satisfaction. The determination of the priorities of the LR is an important issue during QFD process for product or service design. For this reason, in this work, an integrated approach integrating fuzzy logic and QFD methods is proposed to identify and prioritize the LR in dairy food industry for the improvement of customer satisfaction. In addition, a case study in Turkish dairy food industry is given to illustrate the proposed approach for potential readers