Abstract
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 (Formula presented.) and (Formula presented.) 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 (Formula presented.) and (Formula presented.) control charts are developed for the case that the population parameters (μ and σ) are known.
| Original language | English |
|---|---|
| Pages (from-to) | 509-520 |
| Number of pages | 12 |
| Journal | International Journal of Computational Intelligence Systems |
| Volume | 4 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - Jun 2011 |
Bibliographical note
Publisher Copyright:© 2011, the authors.
Keywords
- (Formula presented.) control chart
- Fuzzy
- fuzzy standard deviation
- α-cut
- α-level fuzzy midrange