Number: 025-0620 A New Approach to Design of Fuzzy Multi-Attribute Control Charts by Anjani Kumar, K. V. N. S And Mohapatra, P. K. J Indian Institute of Technology, Kharagpur Department of Industrial Engineering and Management, Kharagpur 721302, India [email protected] (*), [email protected] (**) POMS 23 Annual Conference Chicago, Illinois, U. S. A. April 20 to April 23, ...

quality control plays an important role in increasing the product quality. fuzzy control charts are more sensitive than shewhart control chart. hence, the correct use of fuzzy control chart leads to producing better-quality products. this area is complex because it involves a large scope of industries, and information is not well organized. in this research, we provide a literature review of th...

This paper is about how to control the quality of microarray expression data. Since gene-expression microarrays have become almost as widely used as measurement tools in biological research, we survey microarray experimental data to see possibilities and problems to control microarray expression data. We use both variable measure and attribute measure to visualize microarray expression data. Ac...

Attribute control charts (e.g., charts for counts and proportions) have discrete jumps in the quantities plotted on the charts, which means that the chart designer has only a discrete set of unique control limits to choose from when designing a chart. These charts have a small set of in-control Average Run Lengths (ARLs) from which to determine the appropriate control scheme. In a chart with ra...

One of the existing problems of multi-attribute process monitoring is the occurrence of high number of false alarms (Type I error). Another problem is an increase in the probability of not detecting defects when the process is monitored by a set of independent uni-attribute control charts. In this paper, we address both of these problems and consider monitoring correlated multi-attributes proce...

For processes concerning attribute data with (very) small failure rate p, often negative binomial control charts are used. The decision whether to stop or continue is made each time r failures have occurred, for some r ≥1. Finding the optimal r for detecting a given increase of p first requires alignment of the charts in terms of in-control behavior. In the present paper binomial charts are sub...

Control charts, known for more than 80 years, have been important tools for business and industrial manufactures. Among many different types of control charts, the attribute control chart (np-chart or p-chart) is one of the most popular methods to monitor the number of observed defects in products, such as semiconductor chips, automobile engines, and loan applications. The attribute control cha...

For processes concerning attribute data with (very) small failure rate p, often negative binomial control charts are used. The decision whether to stop or continue is made each time r failures have occurred, for some r ≥ 1. Finding the optimal r for detecting a given increase of p first requires alignment of the charts in terms of in-control behavior. In the present paper binomial charts are su...