Joint <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mover accent="true"><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mo>-</mml:mo></mml:mover></mml:mrow></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:msup><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn fontstyle="italic">2</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math> Control Charts Optimal Design Using Genetic Algorithm

Ramadan, Saleem Z.

doi:10.1155/2018/6516879

Cited by 4 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is mainly used to monitor the arithmetic mean of samples of size n taken successively at a given time and variable quality characteristics. [32][33][34] • CUSUM chart was initially introduced by Page 35 for detecting the cumulative sum of the deviation of sample values from the desired target value. It is very sensitive for detecting small and medium shifts in quality characteristics, compared with Shewhart control charts that work well for detecting large shifts.…”

Section: Control Charts Being Optimizedmentioning

confidence: 99%

Optimization design of control charts: A systematic review

Haridy,

Maged,

Alherimi

et al. 2024

Quality & Reliability Eng

View full text Add to dashboard Cite

In today's competitive landscape, fulfilling customer expectations and achieving a competitive edge are crucial for business success. These objectives can be attained by effective monitoring in both manufacturing and service sectors to enhance quality, reduce variation, and augment productivity. The control chart, a widely used tool for this purpose, has attracted significant attention from researchers for its ability to detect anomalies and manage out‐of‐control situations. The optimization of control charts, a central focus of this review, not only enhances the detection effectiveness but also maintains the desired false alarm rate, thus ensuring efficient process control without additional cost, complexity, or operational challenges for shop floor personnel. The optimization process involves adjusting charting parameters like the sample size, sampling interval, and control limits within a hypothesis testing framework, thereby achieving optimal system performance. Numerous optimization models have been developed to enhance control chart performance. This paper introduces a classification scheme to analyze and categorize the existing research on control chart optimization. By conducting a thorough review of more than 240 articles, the study pinpoints research gaps and offers valuable insights, thereby advancing the future research in this domain.

show abstract

Section: Control Charts Being Optimizedmentioning

confidence: 99%

Optimization design of control charts: A systematic review

Haridy,

Maged,

Alherimi

et al. 2024

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…Some scholars made the improvements and applications for SPC. For example, He et al [18], Zhang and Cheng [19], and Ramadan [20] improved different control charts based on different practical demands. He et al [21,22] proposed, respectively, the risk analysis method and the optimization model for quality control of the proces by combing the SPC method.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Risk Analysis in Metro Construction Using Statistical Process Control

Wang

Zheng

2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Metro construction is normally carried out in complex engineering geological environment, so it can generate various risk events. In the process of metro construction, a scientific risk dynamic analysis is indispensable to reduce and control risks. In order to analyze the risk in metro construction more scientifically and reasonably, in this study, a new risk dynamic analysis method for metro construction is proposed using statistical process control. The method can analyse the risk level according to the process’s capacity index and identify the characteristics of risk variation according to the statistical control chart. The risk level and the characteristic of risks may vary with dynamical updating of monitoring data, so the conclusion of risk evaluation for a time interval can be drawn and corresponding safety measures can be ascertained. The method ushers statistical process control, so the random factors in risk evolution can be considered fully. Then, the method is applied to the risk analysis of shield construction under the Beijing-Tianjin intercity railway in Beijing Metro Line 8, a typical risk problem in the traffic construction. The variation of the risk level and the characteristic of risks can be evaluated reasonably because the dynamical randomness is considered. Moreover, whether risk control measures should be taken and what the effective measures are can be ascertained explicitly.

show abstract