References of "Faraz, Alireza"
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See detailECONOMICALLY DESIGNED HOTELLING’S T2 CONTROL CHART USING VSICL SCHEME
torabian, Mohsen; Moghadam, M. B.; Faraz, Alireza ULg

in Arabian Journal for Science & Engineering (2010), 35(2), 251263

The Hotelling’s T2 control chart, is the most widely used multivariate procedure for two or more related quality characteristics, but it’s power lacks the desired performance in detecting small to ... [more ▼]

The Hotelling’s T2 control chart, is the most widely used multivariate procedure for two or more related quality characteristics, but it’s power lacks the desired performance in detecting small to moderate shifts. Recently, the variable sampling intervals (VSI) control scheme in which the length of successive sampling intervals is determined upon the preceding T2 values has been proved to have a very good performance on detecting small to moderate shifts when it is compared to the original T2 control chart . Moreover, it has been shown that the VSI scheme is more economical than the classical one. This paper studies the economic consequences of a new control scheme named variable sampling intervals and control limits (VSICL) in that the sampling interval h and control limit k vary between minimum and maximum values while keeping the sample size fixed over time. We apply the cost model proposed by Costa and Rahim [8]. Furthermore, we assume that the length of time that the process remains in control is exponentially distributed which allows us to apply the Markov chain approach for developing the cost model. We apply genetic algorithm to determine the optimal values of model parameters by minimizing the cost function. Finally, both VSI and VSICL T2 control charts are compared with respect to the expected cost per unit time. [less ▲]

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See detailAn application of fuzzy random variables to control charts
Faraz, Alireza ULg; Shapirob, Arnold

in Fuzzy Sets & Systems (2010), 161(20), 26842694

The two most significant sources of uncertainty are randomness and incomplete information. In real systems, we wish to monitor processes in the presence of these two kinds of uncertainty. This paper aims ... [more ▼]

The two most significant sources of uncertainty are randomness and incomplete information. In real systems, we wish to monitor processes in the presence of these two kinds of uncertainty. This paper aims to construct a fuzzy statistical control chart that can explain existing fuzziness in data while considering the essential variability between observations. The proposed control chart is an extension of Shewhart ¯X − S2 control charts in fuzzy space. The proposed control chart avoids defuzzification methods such as fuzzy mean, fuzzy mode, fuzzy midrange, and fuzzy median. It is well known that using different representative values may cause different conclusions to be drawn about the process and vague observations to be reduced to exact numbers, thereby reducing the informational content of the original fuzzy sets. The out-of-control states are determined based on a fuzzy in-control region and a simple and precise graded exclusion measure that determines the degree to which fuzzy subgroups are excluded from the fuzzy in-control region. The proposed chart is illustrated with a numerical example. [less ▲]

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See detailConstructing a fuzzy Shewhart control chart for variables when uncertainty and randomness are combined
Faraz, Alireza ULg; Kazemzadeh, R. B.; Moghadam, M. B. et al

in Quality & Quantity (2010), 44(5), 905-914

In this paper we introduce a fuzzy chart for variables which is used in situations when uncertainty and randomness are combined. It is showed that the Shewhart chart’s control limits must be adjusted in ... [more ▼]

In this paper we introduce a fuzzy chart for variables which is used in situations when uncertainty and randomness are combined. It is showed that the Shewhart chart’s control limits must be adjusted in these situations. However, this chart is based on a fuzzy acceptance region and this method arises when a decision should be made by referring to the grade of a sample statistic belonging to the fuzzy acceptance region. [less ▲]

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See detailStatistical process control and delivery chains
Faraz, Alireza ULg; Kazemzadeh, R.B.

in International Journal of Industrial Engineering & Production Research (2010), 21(3), 55-66

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See detailThe Optimal Design of the VSI T2 Control Chart
Faraz, Alireza ULg; Kazemzadeh, R. B.; Heuchenne, Cédric ULg et al

in Journal of Iranian Statistical Society (2010), 9(1), 1-19

Recent studies have shown that the variable sampling interval (VSI) scheme helps practitioners detect process shifts more quickly than the classical scheme (FRS). In this paper, the economically and ... [more ▼]

Recent studies have shown that the variable sampling interval (VSI) scheme helps practitioners detect process shifts more quickly than the classical scheme (FRS). In this paper, the economically and statistically optimal design of the VSI T2 control chart for monitoring the process mean vector is investigated. The cost model proposed by Lorenzen and Vance (1986) is minimized through a genetic algorithm (GA) approach. Then the effects of the costs and operating parameters on the optimal design (OD) of the chart parameters and resulting operating loss through a fractional factorial design is systematically studied and finally, based on the ANOVA results, a Meta model to facilitate implementation in industry is proposed to determine the OD of the VSI T2 control chart parameters from the process and cost parameters [less ▲]

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See detailA Meta Model to Optimal Design the VSI T2 Chart
Faraz, Alireza ULg; Heuchenne, Cédric ULg; Saniga, E.

Report (2010)

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See detailStatistical design of the Hotelling’s T2 control chart with variable ratio sampling scheme using genetic algorithms
Torabian, M.; Faraz, Alireza ULg; Seif, A.

in Journal of Applied Mathematics (2009), 6(22), 77-89

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 ... [more ▼]

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 studies the statistical design of the T2 chart with variable ratio sampling 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. [less ▲]

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See detailImproving the power of out of control states detection in multivariate processes using variable sampling intervals policy
Seif, A.; Faraz, Alireza ULg; Torabian, M.

in Journal of Applied Mathematics (2009), 23

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 ... [more ▼]

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 [less ▲]

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See detailHotelling’s T 2 control chart with two adaptive sample sizes
Faraz, Alireza ULg; Moghadam, M. B.

in Quality & Quantity (2009), 43

Some quality control schemes have been developed when several related quality characteristics are to be monitored. The familiar multivariate process monitoring and control procedure is the Hotelling’s T 2 ... [more ▼]

Some quality control schemes have been developed when several related quality characteristics are to be monitored. The familiar multivariate process monitoring and control procedure is the Hotelling’s T 2 control chart for monitoring the mean vector of the process. It is a direct analog of the univariate shewhart ¯ x chart. As in the case of univariate, the ARL improvements are very important particularly for small process shifts. In this paper, we study the T 2 control chart with two-state adaptive sample size, when the shift in the process mean does not occur at the beginning but at some random time in the future. Further, the occurrence time of the shift is assumed to be exponentially distributed random variable. [less ▲]

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See detailHotelling’s T2 control chart with two adaptive sample sizes
Faraz, Alireza ULg; Moghadam, M. B.

in Quality & Quantity (2009), 43(6), 903-913

Some quality control schemes have been developed when several related quality characteristics are to be monitored. The familiar multivariate process monitoring and control procedure is the Hotelling’s T 2 ... [more ▼]

Some quality control schemes have been developed when several related quality characteristics are to be monitored. The familiar multivariate process monitoring and control procedure is the Hotelling’s T 2 control chart for monitoring the mean vector of the process. It is a direct analog of the univariate shewhart ¯ x chart. As in the case of univariate, the ARL improvements are very important particularly for small process shifts. In this paper, we study the T 2 control chart with two-state adaptive sample size, when the shift in the process mean does not occur at the beginning but at some random time in the future. Further, the occurrence time of the shift is assumed to be exponentially distributed random variable. [less ▲]

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See detailFuzzy Control Chart A Better Alternative for Shewhart Average Chart
Faraz, Alireza ULg; MOGHADAM, M. B.

in Quality & Quantity (2007), 41(3), 375-385

This paper through a real illustrative example and a power test shows that designing a fuzzy control chart for process average of a continuous (variable) quality characteristic with a warning line is a ... [more ▼]

This paper through a real illustrative example and a power test shows that designing a fuzzy control chart for process average of a continuous (variable) quality characteristic with a warning line is a better alternative to Shewhart ¯X chart in many respects, like providing better neural view to inspectors, offering different strategic options for company to choose, detecting the desire shifts more quickly, and more sensibility to small shifts without any complexity augmentation to the chart. [less ▲]

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See detailHotelling's T 2 control chart with double warning lines
Faraz, Alireza ULg; Parsian, Ahmad

in Statistical Papers (2006), 47(4), 569-593

Recent studies have shown that the T 2 control chart with variable sampling intervals (VSI) and/or variable sample sizes (VSS) detects process shitis faster than the traditional T 2 chart. This article ... [more ▼]

Recent studies have shown that the T 2 control chart with variable sampling intervals (VSI) and/or variable sample sizes (VSS) detects process shitis faster than the traditional T 2 chart. This article extends these studies for processes that are monitored with VSI and VSS using double warning lines ( T 2 - DWL ). It is assumed that the length of time the process remains in control has exponential distribution. The properties of T 2- DWL chart are obtained using Markov chains. The results show that the T 2 - DWL chart is quicker than VSI and/or VSS charts in detecting almost all shifts in the process mean. [less ▲]

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See detailInvestigation of the range and effective factors on protein energy malnutrition based on nutritional classification of rural zone of Arak in children less than five years
Jafarinia; Faraz, Alireza ULg; Akhoundzadeh, Sh. et al

in Arak Medical University Journal (2003), 6(2(23)), 7-13

Introduction: The protein-energy malnutrition is the most prevalent cause of malnutrition and always occures in infants and children under 5 years old. According to the harmful effects of malnutrition ... [more ▼]

Introduction: The protein-energy malnutrition is the most prevalent cause of malnutrition and always occures in infants and children under 5 years old. According to the harmful effects of malnutrition, such as high mortality, inability of learning and decrease of mental and physical ability, reconnoite and study of malnutrition qualification is very important. Material and Methods: This analytic cross-sectional study was carried out on 811 children under 5 years old who were living in rural zone of Arak. Based on health files; clinical inspections and interview with mothers a checklist was fulfilled for every child. Data were analysed by EPI6 software. Results: In this study, none of the children had stricken with Kwashiorkor or Marasmic-Kwashiorkor. Undernourished were seen in 7.39% and Marasmus were also seen in 1.1 % of subjects. The prevalence of malnutrition was 7.5% in Wellcome, 33.55% in Waterlow-Stunting, 23.05% in Waterlow-Wasting and 30.94% in Gomez classification. There was statistically correlation between malnutririon and respiratory infection rate, milstones, birthweight, maturity and begning of helpfood (P<0.05). Conclusion: Considering the results, the most rate of malnutrition in each classification was mild. It is suggested that increasing of familial awareness is an important factor for preventing from next damages and additional treatment costs. [less ▲]

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