M.Phil / MS
Permanent URI for this collectionhttps://repository.cuilahore.edu.pk/handle/123456789/63
This collection archives the complete set of theses produced by students of the COMSATS University Islamabad, Lahore Campus.
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Item Hybrid Exponentially Weighted Moving Average Control Chart Using Ranked Set Sampling(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2018) Navara Shabbir; SP17-RST-001; LHR TP 5301; Dr. Muhammad Noor-ul-AminIn manufacturing process, the products are subjected to variations which directly impact the quality of a product e.g. in the process of filling juice bottles, the amount of juice filled may not exactly the same. Quality control combined with statistical techniques is called statistical process control (SPC). To achieve the level of perfection in a production procedure SPC play an important role, it generally contains tools for monitoring the dissimilarities caused during a production process. SPC has a direct impact in increasing the quality of a product by reducing the amount of variation and enabling it to attain desired satisfaction level.Item Induction of Measurement Error in Acceptance Sampling Plans(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2019) Sajid Ali Naqvi; SP17-RST-004; LHR TP 5741; Dr. Muhammad Noor-ul-AminMeasurement error practically exists to a certain degree and part and parcel of measurement process for quantitative observations. Measurement error cause contamination and result in ineffectiveness of the scale sampling plan. In present study the effect of measurement error is investigated for EWMA based scale sampling plan using a linear covariate method when population standard deviation is known or unknown. The same effect is also examined in the presence of auxiliary information when measurement of quality characteristic is not feasible or accessible. Multiple measurements method is a remedy to minimize the effect of measurement error. The variance of measurement error component becomes zero for infinite number of multiple measurements. A reasonable and economical number of multiple measurements may restore the effectiveness and utilization of EWMA based scale sampling plans with or without auxiliary information when population standard deviation is known or unknown. Tables are provided for various values of model parameters for industrial useItem Data-Driven Sample Size Adaptation in EWMA Control Chart for Improved Monitoring(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Gulmakai; CIIT/FA23-RST-002/LHR; Dr. Muhammad Noor-ul-Amin; LHR TP 9873Statistical Process Control (SPC) serves as a fundamental methodology for monitoring and maintaining stability in manufacturing processes. In this study, an Adaptive Sample Size-based Exponentially Weighted Moving Average (ASEWMA) control chart is proposed to enhance the detection of process shifts by dynamically adjusting the sample size in response to observed process behavior. The adaptability of the sample size enables the control chart to respond more efficiently to both small and moderate shifts while conserving computational and sampling resources when the process is stable. Further, the proposed control chart is evaluated in the presence of measurement error. The performance of the ASEWMA control chart is rigorously evaluated through extensive Monte Carlo simulations. Key performance indicators such as the Average Run Length (ARL) and Standard Deviation of Run Length (SDRL) are employed to assess its effectiveness in various shift scenarios. Comparative analyses demonstrate that the ASEWMA chart consistently outperforms traditional control charts, including the Fixed Sample Size EWMA (FEWMA) and Variable Sample Size EWMA (VEWMA), particularly in detecting small shifts in the process mean. The findings of this research highlight the ASEWMA chart’s potential to provide a practical and efficient tool for quality monitoring in industrial settings. By achieving a desirable balance between sensitivity to shifts and computational efficiency, the proposed method facilitates the timely detection of process changes while maintaining robustness under in-control conditions