Evaluating the Packing Process in Food Industry Using Fuzzy and [Stilde] Control Charts

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 X and S 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 X and S control charts are developed for the case that the population parameters (µ and σ) are known.

Construction of Fuzzy C Control Charts Based on Fuzzy Rule Method

“…Collected fuzzy data are shown in Table 1 as triangular fuzzy numbers. The fuzzy upper and lower control limits and fuzzy center line of the fuzzy c -control chart are calculated by using Eg (6)(7)(8) and given in the following equations.…”

“…There are some papers on fuzzy variable control charts and their applications. Rowlands and Wang [1] El-Shal and Morris [2] Zarandi et al [3] Erginel [4] Şentürk and Erginel [5], Şentürk [6], Erginel et al [7]. There are some publications on fuzzy attribute control charts and their applications in literature: Raz and Wang [8] Wang and Raz [9].…”

Monitoring the Process Based on Belief Statistic for Neutrosophic Gamma Distributed Product

“…The best-fitted distribution is the normal distribution for the continuous data when collected in groups or when the form of the distribution is known. In general, Sentürk [22] developed the fuzzy control chart for monitoring the food industry using fuzzy X and S. El-Shal and Morris [23] suggested a fuzzy rule-based algorithm for quality improvement monitoring through control charts. Rowlands and Wang [24] studied the fuzzy logic operation and its functioning in the control chart literature.…”

Optimizing and evaluating performance quality control of the production process of disposable essentials using approach vague goal programming

“…A fuzzy method is provided for vague data in monitoring the mean and variance of fuzzy charts by [2]. One method for calculating fuzzy standard deviations is to obtain a graph for cases provided by ̅ , as well as explained by the theoretical structure of fuzzy control ̅ which is known as local parameters [3]. Several approaches have been proposed to optimize process performance with multiple responses [4].…”