Muhammad Mashuri

Pendeteksian Outlier dan Penentuan Faktor-Faktor yang Mempengaruhi Produksi Gula dan Tetes Tebu dengan Metode Likelihood Displacement Statistic-Lagrange

This paper investigates a development of single machine batch scheduling for multi items with dependent processing time. The batch scheduling problem is to determine simultaneously number of batch ( ), which item and its size allocated for each batch, and processing sequences of resulting batches. We use total actual flow time as the objective of schedule performance. The multi item batch scheduling problem could be formulated into a biner-integer nonlinear programming model because the number of batch should be in integer value, the allocation of items to resulting batch need binary values, and also there are some nonlinearity on objective function and constraint due to the dependent processing time. By applying relaxation on the decision variable of number of batch ( ) as parameter, a heuristic procedure could be applied to find solution of the single machine batch scheduling problem for multi items.

T 2 Control Chart based on Successive Difference Covariance Matrix for Intrusion Detection System

The Intrusion detection is a process to monitor the events taking place in a computer system or network and analyze the monitoring results to find signs of intrusion. One of alternative solutions for intrusion detection is the usage of statistical methods that Statistical Process Control especially the control charts.. In this research, the Hotellings T 2 chart performance for intrusion detection is improved using the Successive Difference Covariance Matrix where the control limits will be calculated using Kernel Density Estimation. The proposed method using T 2 based on Kernel Density Estimation control limit outperforms other approaches both in training and testing dataset.

Perfomance Fuzzy Multinomial Control Chart

One of the most useful tool in Statistics Process Control is control chart. This technique has been used widely in industry and services. One of the most simple attribute control chart is p chart, when the item classified into to categories. In its development, if each item of quality characteristics classify in more than two categories multinomial control chart more appropriate. The classify such as Excellent, Good, Fair and Bad. However, if there is vagueness of the classification for each item, the fuzzy multinomial control chart is more appropriately used. By using triangular fuzzy number to calculate the representative value and simulation study, the control chart will be evaluated based on the value of average run length. For small shift of parameter pi , the value of average run length when the process is in control closed to 370 and when the process out of control the value of average run length are decrease. Based on this this value, it shown that the Fuzzy Multinomial control chart is sensitive.

Multioutput Least Square SVR Based Multivariate EWMA Control Chart

Multioutput least square SVR has ability to remove serial correlation of process by mapping multivariate input space to multivariate output space. The aim of this research is to propose multioutput least squares SVR based multivariate EWMA control chart to monitor small change of multivariate autocorrelated process. VARMA model with additive and innovative outliers are generated to investigate the performance of proposed control chart. Simulation studies empirically show that multioutput least squares SVR based multivariate EWMA control chart detect either single or consecutive additive outlier takes place at different time in each variable accurately. On the contrary, single innovative outlier in each variable that occurs either at different time or at the same time is detected by multioutput least squares SVR based multivariate EWMA control chart as double out-of control signals.

T2 hotelling fuzzy and W2 control chart with application to wheat flour production process

A good product must satisfy the standard of corresponding quality characteristic. A product is categorized as defect when its quality characteristic out of its specification limit. The measurement of quality characteristic may produce error because it measures quality around its actual value instead of the actual value itself. This measurement error can produce an ambiguity. Some approaches have been developed to tackle this ambiguity problem. This research employed T2 Hotelling Fuzzy and W2 control chart with probability approach to deal with this issue. The proposed methods were applied to monitor the wheat flour production process. Three variables, multivariate processes, were used to identify the product quality such as moisture, gluten, and ash. The membership function used fuzzy approach is a combination of trapezoidal and linear curves. This research concluded that W2 control chart with probability approach performed better than T2 Hotelling Fuzzy control chart.

Bootstrap-based maximum multivariate CUSUM control chart

ABSTRACT Maximum multivariate cumulative sum (Max-MCUSUM) is one of the single control charts that plot single statistic as a representation of mean vector and covariance matrix. The Max-MCUSUM statistic has unknown specific distribution. The objective of this paper is to propose bootstrap-based Max-MCUSUM control chart for which reference value is predetermined in Phase I monitoring process. For various numbers of quality characteristics and correlation coefficients, the control limits estimated using bootstrap approach are presented in this paper. Furthermore, the average run lengths of bootstrap-based Max-MCUSUM control chart prove that the proposed control chart tends to be effective for monitoring the small shift in both mean and variance of a process. The illustrative examples are provided to demonstrate the applications of the proposed control chart for both simulation and real data.

Multivariate control chart based on PCA mix for variable and attribute quality characteristics

ABSTRACT Two types of control charts exist based on different quality characteristics: variable and attribute. These characteristics are commonly monitored using separate procedures. Only a few studies focused on the utilization of control charts to monitor a process with mixed characteristics. This study develops a new concept of the control chart based on a Principal Component Analysis (PCA) Mix, that is a PCA method that can jointly handle continuous and categorical data. The Kernel Density Estimation (KDE) method is used to estimate the control limit. Through simulation studies, the performance of the proposed chart is evaluated using the Average Run Length (ARL). control limits obtained from KDE produce a stable ARL0 at ~ 370 for For the shifted process, the proposed chart demonstrates excellent performance for an appropriate number of principal components used. Applications of the simulated process and real cases show that the proposed chart is sensitive to monitoring the shifted process.

Multioutput least square SVR-based multivariate EWMA control chart: The performance evaluation and application

Abstract Autocorrelation leads to a bias estimator of standard control charts. It is important to develop control chart that allows autocorrelation and to evaluate its performance. The objective of this paper is to evaluate the performance of multioutput least square support vector regression (MLS-SVR)-based multivariate exponentially weighted moving average (MEWMA) control chart for monitoring multivariate autocorrelated data. For first order of vector autoregressive (VAR) and first order of vector moving average data, the proposed control chart tends to yield stable in-control average run length at about 200. The proposed control chart becomes more insensitive due to the increase of MEWMA smoothing parameter. In the real application, the proposed method is successfully applied to monitor water turbidity and chlorine residual data in the drinking water manufacturing process.