Control charts are powerful tools of statistical process control (SPC) used for monitoring processes. Control charts are not only used to detect changes in process, but also enable the quality engineers to prepare the system for an appropriate response before production of the faulty products. The main objective of most researches on control charts is developing a favorable and cost-effective design as well as most appropriate sampling methods. In most of the researches that have been done in this field, the control charts are designed based on simple random sampling (SRS). However, there are many charts in the literature of statistical process control that are not based on SRS and use alternative methods such as ranked set sampling (RSS) or generalized methods. The RSS method is an alternative to SRS when taking actual measurement of the quality characteristic is costly or production of the faulty products is expensive. In this paper, in order to improve the performance of mixed EWMA-CUSUM (MEC) and mixed CUSUM-EWMA (MCE) control charts in estimating the population mean, ranked set sampling method rather than common simple random sampling method is used to design these mixed control charts. The performances of the proposed MEC-RSS and MCE-RSS control charts are evaluated through simulation studies using the AVERAGE run length (ARL) criterion. Moreover, the performances of the proposed control charts are compared with those of CUSUM-SRS, CUSUM-RSS, EWMA-SRS, EWMA-RSS, MEC-SRS, and MCE-SRS control charts. The results revealed that RSS method improves control charts performance compared to SRS method because it provides an efficient estimation of the mean and variance of the population. In addition, using the ranked set sampling scheme makes the proposed mixed control charts more sensitive to detecting the small and moderate shifts in the process mean than the other corresponding classical control charts. Moreover, based on the same sampling methods, the performances of mixed control charts are better than those of the single CUSUM and EWMA control charts.

The cost parameters in economic-statistical models of control charts are usually assumed to be deterministic in the literature. Considering uncertainty in the cost parameters of control charts is very common in application. So, several researchers used scenario-based approach for robust economic-statistical design of control charts. In this paper, we specifically concentrate on the MULTIVARIATE EXPONENTIALLY WEIGHTED MOVING AVERAGE ((MEWMA)) control chart and consider interval uncertainty in the cost parameters of the (MEWMA) control chart and develop a robust economic-statistical design of the (MEWMA) control chart by using interval robust optimization technique. Meanwhile, the Lorenzen and Vance cost function is used, and to calculate the AVERAGE run length criterion, the Markov chain approach is applied. Then, genetic algorithm for obtaining optimal solution of the proposed robust model is used and effectiveness of this model is illustrated through a numerical example. Also, a comparison with certain situation of the cost parameters is performed. Finally, a sensitivity analysis is done to investigate the effect of changing the intervals of cost parameters of the Lorenzen and Vance model on the optimal solutions. Furthermore, a sensitivity analysis on the other certain cost parameters of the Lorenzen and Vance model is done.

In this paper, a new nonparametric control chart based on two EWMA statistics is proposed for monitoring process deviation from the target value. The performance comparison of the proposed chart with the existing nonparametric EWMA sign control chart and a nonparametric CUSUM mean control chart are made by using out-of-control AVERAGE run length. The simulation study showed the superiority of the suggested chart over the four other parametric and nonparametric charts. An empirical illustration is also provided for performance evaluation of the proposed chart. By comparing the proposed chart with the existing charts, it is concluded that the proposed chart is more sensitive in detecting a small shift in the process.

This study proposed a dual EXPONENTIALLYWEIGHTED MOVING AVERAGE (EWMA) statistics based nonparametric sign control chart to monitor the process deviation from the targeted value. The performance of the proposed chart is compared with the existing nonparametric version of the EWMA sign and Cumulative Sum (CUSUM) mean control charts by using out-of-control AVERAGE run length for various shifts in the process. The simulation study showed the superiority of the suggested chart over the existing counterparts. A real dataset is also considered for the application of the proposed chart.

Several researchers have investigated the e ect of measurement errors on adaptive Shewhart charts. However, the e ect of measurement errors on the performance of Variable Sample Size EXPONENTIALLY WEIGHTED MOVING AVERAGE (VSS EWMA) control charts has not been evaluated yet. In this regard, the present study aims to investigate the performance of the VSS EWMA chart in the presence of measurement errors using a linear covariate error model and Markov chain method. The results indicated that the presence of measurement errors could signi cantly a ect the performance of the VSS EWMA chart. In addition, the e ect of taking multiple measurements for each item in a subgroup on the performance of the VSS EWMA chart was evaluated. Moreover, the performance of the VSS EWMA control chart was compared with those of several other control charts in the presence of measurement errors. Finally, an illustrative example was presented to demonstrate the application of the VSS EWMA control chart with measurement errors.

One of the most important quality characteristics in a production process is the product lifetime. The production of highly reliable products is a concern of manufacturers. Since it is time-consuming and costly to measure lifetime data, designing a control chart seems difficult. To solve the problem, lifetime tests are employed. In the present study, one-sided and two-sided EWMA control charts are designed under a type II censoring (failure censoring) life test. Product lifetime is a quality characteristic dealt with in this study. It is assumed to follow the Weibull distribution with a fixed shape parameter and a variable scale parameter. In order to design a control chart, first, the control chart limits are calculated for different parameters, and then the AVERAGE Run Length (ARL) in the out-of-control state is used to evaluate the performance of the proposed control chart. Next, a comprehensive sensitivity analysis is performed for the different parameters involved. The computational results show that the one-sided control chart has better performance to detect the shift of lifetime data than the two-sided control chart. The AVERAGE run length curve of the two-sided control chart is biased, while that of the one-sided control chart is unbiased. A very effective parameter that increases the performance of a control chart is found to be the number of failures in the failure censoring process. Finally, simulated and real examples are provided to show the performance of the proposed control chart.

Purpose: This paper aims to study the effect of inertia on the performance of the EWMA control chart for attribute characteristics and to investigate the effects of inertia properties on control charts with attribute characteristics. Design/methodology/approach: By calculating the inertia criterion, the effectiveness of the control chart can be measured in identifying changes and how one can reduce the inertial properties of the control charts. MATLAB software has been used to conduct simulation studies. The effect of inertia on the performance of an EXPONENTIALLY WEIGHTED MOVING AVERAGE control chart and modified EXPONENTIALLY WEIGHTED MOVING AVERAGE control chart for observations caused by correlated processes based on Poisson distribution has been investigated. Findings: After calculating and analyzing the results, it was determined that the modified exponential MOVING AVERAGE control chart performs better than the exponential MOVING AVERAGE control chart based on the Poisson distribution in the worst-case signal resistance. Also, by decreasing the parameter λ, the inertia value of the control charts increased. Research limitations/implications: Other control charts that follow different distributions can be used, or the effect of inertia on the performance of control charts in multivariable processes can be considered as a subject for further study. Practical implications: After analyzing the positive properties of inertia, it is possible to increase productivity in production processes. This research purposed new indicators to evaluate the performance of control charts in terms of inertial properties. Social implications: In the field of manufacturing industries, it is possible to increase productivity by examining the appropriate characteristics of inertia. On the other hand, regarding the use of control charts for monitoring chemical and petrochemical processes and evaluating the inertia index, it is possible to reduce the variability of the process in long term, which will have an impact on environmental issues. Originality/value: Measuring the inertia of the EXPONENTIALLY WEIGHTED MOVING AVERAGE control chart when the observations are independent and follow the Poisson distribution, in addition to measuring the inertia of the EXPONENTIALLY WEIGHTED MOVING AVERAGE control chart in the presence of correlated observations distinguishes this research from previous studies.

Literature indicates that the statistical process control approach is capable of improving the quality of surgical processes. The use of this approach in the case of real surgical processes is an attractive contribution for researchers. Since risk factors of each patient before a surgery are di, erent potentially, the use of a control chart approaching risk-adjusted is addressed by researchers. The sensitivity of a model to small shift types of the mortality odds ratio is very important for monitoring the quality of a surgery process. Although di, erent schemes have been proposed by researchers to monitor the performance of a medical team, based on the authors' knowledge this research for the , rst time proposes a risk-adjusted double EXPONENTIALLY WEIGHTED MOVING AVERAGE (RADEWMA) approach to monitor the mortality rate. The numerical performance analysis of this paper based on AVERAGE run length (ARL) term for the cardiac surgery addresses that the sensitivity of the proposed risk-adjusted scheme is superior compared to other risk-adjusted chart of literature. In this research the performance of the proposed model is compared comprehensively with risk adjusted P chart (RAP), risk adjusted EXPONENTIALLY WEIGHTED MOVING AVERAGE (RAEWMA), and risk adjusted cumulative sum (RACUSUM). In this paper for analyzing the performance comparatively, the simulated data is produced based on UK Center for Cardiac Surgery data in the period of 1992-1994. This data is referred to 2218 patients with heart surgery. In this simulation to estimate the parameters of the logistic regression, Parsonnet number is used as an explanatory variable. The comparative performance analysis is reported for di, erent shift cases. The comparative report is analyzed statistically for di, erent values of parameters. For statistical analyzing SPSS software is also used. The new proposed scheme to monitor the mortality odds ratio, can be used in di, erent surgeries of a hospital e, ectively.

Background: There are few published studies that use real data testing to examine the performance of outbreak detection methods. The aim of this study was to determine the performance of the EXPONENTIALLY WEIGHTED MOVING AVERAGE (EWMA) in real time detection of a local outbreak in Mashhad City, eastern Iran.Methods: The EWMA algorithms (both EWMA1 with l=0.3 and EWMA2 with λ=0.6) were applied to daily counts of suspected cases of measles to detect real outbreak which has occurred in the city of Mashhad during 2010. The performances of the EWMA algorithms were evaluated using a real data testing approach and reported by correlation analysis.Results: Mashhad outbreak was detected with a delay of about 2 to 7 days using EWMA algorithms as outbreak detection method. Moreover, the utility of EWMA2 algorithm in real time detection of the outbreak was better than EWMA1 algorithm.Conclusion: Applying the EWMA algorithm as an outbreak detection method might not be useful in timely detection of the local outbreaks.