Nihal Erginel

Fuzzy exponentially weighted moving average control chart for univariate data with a real case application

Statistical process control (SPC) is an approach to evaluate processes whether they are in statistical control or not. For this aim, control charts are generally used. Since sample data may include uncertainties coming from measurement systems and environmental conditions, fuzzy numbers and/or linguistic variables can be used to capture these uncertainties. In this paper, one of the most popular control charts, exponentially weighted moving average control chart (EWMA) for univariate data are developed under fuzzy environment. The fuzzy EWMA control charts (FEWMA) can be used for detecting small shifts in the data represented by fuzzy numbers. FEWMA decreases number of false decisions by providing flexibility on the control limits. The production process of plastic buttons is monitored with FEWMA in Turkey as a real application.

Construction of a fuzzy QFD failure matrix using a fuzzy multiple-objective decision model

Global competition has forced companies to consider increasing quality and decreasing all costs, including quality costs. When constructing the quality plan for both the incoming raw materials and the manufacturing process, all quality characteristics (QCs) of products or processes cannot be measured or tested because of preventive costs. Companies should decide which QCs are significant and should be measured or tested. This paper presents a methodology for selecting the significant QCs of a product. A quality function deployment failure matrix is proposed, incorporating failure information from design failure mode and effect analysis, related QCs, vague information on technical difficulties, and imprecise cost and time measurements for QCs. In addition, this method uses a fuzzy multiple-objective decision model that addresses two objectives: maximising the priorities of QCs and minimising the technical difficulties within the constraints of limited cost and time. A two-phase approach is used for solving the fuzzy multiple-objective model. A real application to a backhoe loader wheel is presented as an illustration.

Evaluating the Packing Process in Food Industry Using Fuzzy X and S Control Charts

The fuzzy set theory addresses the development of concepts and techniques for dealing with uncertainty or impression conditions. If the collected data from a process include vagueness due to human subjectively or measurement system, fuzzy control charts are available tools for monitoring and evaluating the process. The main contribution of fuzzy control charts is to provide flexibility to the control limits. When sample mean is too close to the control limits and the used measurement system is not so sensitive, the decision may be faulty. In this paper, the fuzzy standard deviation is firstly introduced to obtain fuzzy and [Stilde] control charts and then these fuzzy control charts are employed in food industry to monitor if the processes are under control or not. Additionally, the fuzzy and [Stilde] control charts are developed for the case that the population parameters (μ and σ) are known.

Fuzzy multi-objective decision model for calibration supplier selection problem

Criteria are considered calibration supplier selection in firstly.Calibration cost and calibration time are handled with fuzzy numbers.Fuzzy multi-objective linear model is carried out to select the calibration supplier.Two objects are took into fuzzy multi-objective linear programming model.First objective is achieved 86%, and the second is achieved 87%. Quality products and competitively priced services are crucial in todays global markets. To provide quality at an acceptable price, companies seek out not only raw material and product suppliers but also calibration services suppliers. The calibration of measurement devices is one of the ISO9001 standard requirements for quality. Although companies select and manage their calibration processes as economically as possible, cost is not their only selection criterion. Technical capability, documentation competence, performance history, warranties, and communications are also considered when selecting calibration suppliers. There are numerous criteria and methods in the current literature on supplier selection, but few studies have specifically examined the selection of calibration suppliers.This paper is the first study that specifically examines the selection process for calibration suppliers by utilizing selection criteria that were researched and presented prior to this study. Due to the varied linguistic expressions of criteria and the uncertain model parameters, this paper presents a fuzzy approach for selecting a calibration supplier. The model contains relevant calibration service quality parameters such as the weight of criteria, cost, calibration time, demand, technical capability, and number of experts. This study proposes a fuzzy multi-objective linear programming model that assigns the calibration supplier to the measurement device using two objectives: maximizing the fuzzy weight of the criteria and minimizing the fuzzy calibration cost through fuzzy calibration time, the number of certified experts, the technical capability of the company, and the measurement device demand. A two-phase approach is used to solve the fuzzy multi-objective decision model. Weight, cost, and calibration time are handled as fuzzy numbers for modelling the imprecise data. In an actual application, calibration suppliers are selected for 20 measurement device types and 161 measurement devices with the fuzzy multi-objective linear programming model.

Fuzzy rule-based

In a process using monitoring with p-control charts or np-control charts, due to the uncertainty of the attribute data, traditional control charts may be insufficient. In this case, fuzzy control charts are pertinent control techniques used to capture vagueness. Many studies in the literature are configured with transformation techniques at the decision stage, and in this study, fuzzy control charts are constructed using rules. Therefore, fully fuzzy control charts are first introduced for fuzzy p̃-control charts based on constant sample size and variable sample size, and fuzzy np̃-control charts are subsequently introduced using decision rules for the process state conditions. In addition, “rather in control” and “rather out of control” decisions can be considered in monitoring of the process.

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Airport service quality is a crucial issue in the transportation industry. Although many studies focus on service quality and measurement of the airports, only a small number of studies that consider linguistic expression and fuzzy approach exist in the literature. This study aims to propose a new integrated approach to develop airport service quality. An integrated approach involving fuzzy Quality Function Deployment (QFD) based on SERVQUAL is used to measure airport service quality and to determine fuzzy weights of technical design requirements. Firstly, passengers’ needs are prioritised by using the SERVQUAL method and gap scores, then QFD matrix is constructed by defining fuzzy relationships between passengers’ needs and expectations, and technical design requirements under imprecise knowledge. Technical design requirements are weighted by using fuzzy QFD matrix as fuzzy numbers. Finally, a fuzzy multiple objective decision model is proposed to select the significant technical design requirements with two objectives: maximising the total fuzzy weights of technical design requirements and minimising the fuzzy technical difficulty level under a fuzzy budget constraint. A two-phase approach is used to solve the constructed fuzzy multi-objective decision model. The final results indicate four design requirements named as ‘the number of information board’, ‘capacity of wi-fi’, ‘emergency help button and automation’, and ‘the number of the signboard’, which maximise the total fuzzy weights of technical design requirements and minimise the sum of the fuzzy technical difficulty of each design requirements under the fuzzy budget constraint. This study is a real-world application which was implemented at the Anadolu University Airport in Turkey.

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ABSTRACT This study empirically investigates the states of Six Sigma from a stochastic point of view. By the means of an advanced survey, 97 respondents are asked to rate the effect of Six Sigma on different performance categories, the cost of implementing Six Sigma, the level of enthusiasm and expectations from Six Sigma over 20 years. The autocorrelation and cross-correlation functions of these processes are analyzed to investigate the stages of Six Sigma. Consequently, new concepts namely steady state of Six Sigma and Six Sigma experience functions are introduced which shed light on the life cycle of Six Sigma within the companies.

n\tilde p

Airport service quality is crucial due to the increasing traveling by plane, nowadays. There are some studies on the passengers satisfaction in literature. But lots of them are related to determine the passengers’ needs and expectations and their importance levels. This paper presents the service quality approach for ranking the technical requirements that meet passengers’ needs and expectations for the Anadolu University Airport in Turkey. Firstly, the service quality is measured with the questionnaire. The questionnaire is organized to collect the passenger’s both expectations and perceptions, and is evaluated by using SERVQUAL model. Then, Quality Function Deployment (QFD) approach is used for setting the relationships between the passenger requirements and the technical requirements, and between technical requirements of the airport. Finally, the technical requirements are ranked by the calculation method for the maximum passenger satisfaction.

control charts

Crisp Shewhart control charts monitor and evaluate a process as “in control” or “out of control” whereas the fuzzy control charts do it by using suitable linguistic or fuzzy numbers by offering flexibility for control limits. In this chapter, fuzzy attribute control charts and fuzzy variable control charts are developed and some numeric examples are given.

Designing the airport service with fuzzy QFD based on SERVQUAL integrated with a fuzzy multi-objective decision model

Fuzzy attribute control charts, where data are classified into conforming/nonconforming product units, are used to monitor fuzzy fractions of nonconforming units for variable sample sizes and the fuzzy number of nonconforming units for constant sample sizes. Data defined as quality characteristics can be imprecise due to the subjective decisions of the quality control operator. Type-2 fuzzy set theory deals with ambiguity associated with the uncertainty of membership functions by incorporating footprints and modeling high-level uncertainty. In this paper, the structure of an interval type-2 fuzzy p-control chart and interval type-2 fuzzy np-control chart with constant sample size are developed and applied to real data. The main advantage in using interval type-2 fuzzy sets in control charts is the flexibility allowed in determining control limits for process monitoring by incorporating fuzzy set theory. Therefore, fuzzy control charts with interval type-2 fuzzy numbers afford the decision maker the opportunity to see and detect process defects.