On-line high performance liquid chromatography (HPLC) was used to monitor steady state reactions in three reactors (K=3) over 48.0 h. Different numbers of chromatograms, with J=1981 retention time points, were recorded for each of the three reactors. Peaks for each chromatogram were baseline corrected and aligned using correlation optimized warping (COW). To make a complete three-way data set of I=266 chromatograms a cubic Hermite interpolation was performed. The applied bilinear MULTIVARIATE STATISTICAL PROCESS CONTROL (MSPC) method included the UNFOLDING PCA and the trilinear technique was PARAFAC.UNFOLDING in reactor (K) mode was the most informative. D-charts and Q-charts were applied to the data to determine samples which were out of CONTROL. Confidence limits were then applied to the D and Q-charts and variables with different behaviours from that encapsulated within the reference data set were located. Both bilinear and trilinear methods were found to be useful for PROCESS analysis.

In this paper the delivery times in the delivery chains is modeled and monitored STATISTICALly. The model is developed based on a double warning CONTROL chart using Lorenzen and Vance cost function and it is solved by using the Genetic Algorithm approach. The model is tested using the TNT post services data in the United States of America.

We introduce a method for the STATISTICAL design of a depth-based CONTROL chart, using the percentile-based approach. The proposed CONTROL chart is affine invariant and is asymptotically distribution-free. Generally, the performance of a CONTROL chart is evaluated with the average run length metric. The average run length metric has a geometric distribution skewed to the right with a large standard deviation and may not be a proper measure for evaluating the CONTROL chart. Therefore, we use the STATISTICAL design method of CONTROL charts with the PL approach, which is an improvement and development on classical STATISTICAL design. By employing constraints on average run length, the length of in-CONTROL and out-of-CONTROL performances are guaranteed with predetermined probabilities and we can ensure that the in-CONTROL run length exceeds the desired value and the out-of-CONTROL run length is less than the desired value. Simulation studies show that the proposed CONTROL chart is more efficient than the average run length approach.

The familiar MULTIVARIATE PROCESS monitoring and CONTROL procedure is the Hotelling’s T2 CONTROL chart, a direct analog of the univariate shewhart  chart. But, its efficiency for detecting small to moderate shifts in the PROCESS mean is poor. To improve the power of chart, this paper presents the variable sampling intervals (VSI) scheme. It is assumed that the length of time the PROCESS remains in CONTROL has exponential distribution. The chart is modeled using Markov chains and is optimized using genetic algorithm optimization method. The results show that the T2 chart with variable ratio sampling scheme is quicker than the classical one in detecting almost all shifts in the PROCESS mean.

The usual procedure when employing a T2 CONTROL chart for MULTIVARIATE PROCESS monitoring is to take samples of fixed size n0 every h0 hours from the PROCESS. Recent studies have shown that using variable parameters (VP) schemes results in charts with more STATISTICAL power when detecting small to moderate shifts in the PROCESS mean vector. In this paper, the VPT2 CONTROL chart for monitoring the PROCESS mean vector is economically designed. The cost model proposed by Lorenzen and Vance is used here and is minimized through a genetic algorithm (GA) approach.

Shewhart CONTROL charts are widely accepted as standard tools monitoring manufacturing STATISTICAL PROCESSes. The CONTROL charts have not applied, when the PROCESS distribution is not normal. The bootstrap is one of the resampling methods that can be used in STATISTICAL quality CONTROL without normality assumption. In most of papers, only the percentile bootstrap confidence interval is used for CONTROL limits. In this paper, we apply percentile bootstrap, bootstrap-t, bias corrected accelerated (BCa) and approximate bootstrap confidence interval (ABC) for mean CONTROL limits of STATISTICAL PROCESS. Then, the bootstrap confidence intervals are used and compared for mean CONTROL limits in simulation study. Finally, the bootstrap CONTROL limits are used for mean of CO2 data in Isfahan Zamzam factory.

In STATISTICAL PROCESS CONTROL, traditionally, the assumption is made that successive observations of a quality characteristic are independently distributed. However, in practice, the observations are often serially correlated. This point has received considerable attention in the literature in the last decade and the modified Shewhart and the residuals charts have been proposed to deal with this situation. In this paper, it is investigated how well these CONTROL charts are able to detect a shift in the mean of AR (1) and AR (2) data. It is shown that for negative auto-correlations, the residuals chart is the better of these two and, for positive auto-correlation, it is better to choose the modified shewhart chart. Then, a modification of the residuals chart was made that outperforms both charts in the case of positive autocorrelation. Moreover, in order to improve the performance of the existing CONTROL charts, three kinds of CONTROL charts namely modified EWMA, EWMA residuals and EWMA of modified residuals were developed, Through a simulation study, it is shown that, in terms of negative auto -correlation, the EWMA residuals chart is the best and for positive auto-correlation it is best to choose the EWMA chart of the modified residuals. Finally, an algorithm was proposed to CONTROL AR (1) and AR (2) data. This algorithm was implemented to a real world problem and the results were reported.

In this research, application of STATISTICAL quality CONTROL PROCESS in Kishwood industrial company has been investigated. STATISTICAL quality CONTROL PROCESS includes CONTROL charts and acceptance samplings. In this study, qualitative and quantitave characteristics of raw material and work in progress was investigated using standard tables and CONTROL charts (X 􀴥 , R, P, U) were designed for grooving, drilling, laminating and edge banding PROCESSes. In the raw material and work in progress, medium density fiberboard (16mm) lots, HPL veneer, purchased ABS stripe and the distance of the double hole from the edge of the piece in terms of quantative characteristic were investigated by MIL-STD-414 tables. In addition, purchased veneered particle board lot and the accuracy of the stripe in cabinet bottom parts in terms of qualitative characteristic were investigated by MIL-STD-105E tables. All the results were compatible with the acceptable standards of the company. Since lots data were located in the CONTROL limits, we can conclude that the whole PROCESS had been under CONTROL during the research time and can be used to CONTROL the future of the PROCESS. By calculating the PROCESS capability index (CP), it was found that grooving and drilling PROCESS have a very high capability, so the percentage of waste in these two PROCESSes is very low.

In this research, applications of STATISTICAL quality CONTROL PROCESS in Sima choob Company have been investigated. Sima Choob produces various wood products per order only. STATISTICAL quality CONTROL PROCESS includes all CONTROL investigations and diagrams. In this research, work in progress and the finished products were studied, using standard MIL-STD tables, current STATISTICAL techniques and CONTROL diagrams (X, R, C, and U) of qualitative and quantitative characteristics of their raw materials. The results obtained from STATISTICAL quality CONTROL PROCESS showed that during the research time and in the raw material section, the quality of twin wheels lot, 40Cm rail, Tran's rod, laminated particleboard, and medium density fiberboard was not compatible with the acceptable standards of the company. This leads to return of the raw material as well as imposing more CONTROL on the suppliers of such products. On the other hand, work in progress lot and finished products were compatible with the company's standards. Since data lots were located in the CONTROL limits of assembling, painting and other sections indicated that the whole PROCESS had been under CONTROL during the research time. In general, it can be concluded that using STATISTICAL quality CONTROL PROCESS for the raw materials, work in progress, and the finished products would enhance the quality and the useful life of the products.