In the past few years, there has been an increasing interest in developing project control systems. The primary purpose of such systems is to indicate whether the actual performance is consistent with the baseline and to produce a signal in the case of non-compliance. Recently, researchers have shown an increased interest in monitoring project’s performance indicators, by plotting them on the S...

The control charts are graphical tools and proven techniques to improve the performance of a process. Usually, the processes are not naturally controlled, so the use of control charts will help to reduce the variability and increase the stability of the process. In the traditional approach, control charts with fix sample size and constant sampling intervals were used to identify the changes in ...

Today for the expedition of the identification and timely correction of process deviations, it is necessary to use advanced techniques to minimize the costs of production of defective products. In this way control charts as one of the important tools for the statistical process control in combination with modern tools such as artificial neural networks have been used. The artificial neural netw...

The identification of control chart patterns is very important in statistical process control. Control chart patterns are categorized as natural and unnatural. The presence of unnatural patterns means that a process is out of statistical control and there are assignable causes for process variation that should be investigated. This paper proposes an artificial neural network algorithm to identi...

Control charts are widely used in industry as a tool to monitor process characteristics. Deviations from process targets can be detected based on the evidence of statistical significance. The control chart helps to take decisions such as the need for machine or technology replacement to monitor the process with categorical observations. Fuzzy logic is used to cram the uncertainty and vagueness ...

Economic design of multivariate exponentially weighted moving average (MEWMA) control charts for monitoring the process mean vector involves determining economically the optimum values of the three control parameters: the sample size, the sampling interval between successive samples, and the control limits or the critical region of the chart. In the economic-statistical design, constraints (inc...

This paper presents a control chart pattern recognition system using a statistical correlation coefficient method. Pattern recognition techniques have been widely applied to identify unnatural patterns in control charts. Most of them are capable of recognizing a single unnatural pattern for different abnormal types. However, before an unnatural pattern occurs, a change point from normal to abno...

When an economic-statistical model for a control chart is considered the effect of the choice of the bounds on the average times until a signal on the cost for controlling the process, including the cost with non-conformities produced, and on the design parameters is a natural issue that arises. To have an idea of how the costs and the chart’s parameters are affected by these bounds is an impor...

this paper addresses the design of control charts for both variable ( x chart) andattribute (u and c charts) quality characteristics, when there is uncertainty about the processparameters or sample data. derived control charts are more flexible than the strict crisp case, dueto the ability of encompassing the effects of vagueness in form of the degree of expert’spresumption. we extend the use o...