Yazar "Koyuncu, Melik" seçeneğine göre listele

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    An Approximate Solution for M/G/1 Queues with Pure Mixture Service Time Distributions
    (MDPI, 2025) Koyuncu, Melik; Uncu, Nusin
    This study introduces an approximate solution for the M/G/1 queueing model in scenarios where the service time distribution follows a pure mixture distribution. The derivation of the proposed approximation leverages the analytical tractability of the variance for certain mixture distributions. By incorporating this variance into the Pollaczek-Khinchine equation, an approximate closed-form expression for the M/G/1 queue is obtained. The formulation is extended to service-time distributions composed of two or more components, specifically Gamma, Gaussian, and Beta mixtures. To assess the accuracy of the proposed approach, a discrete-event simulation of an M/G/1 system was conducted using random variates generated from these mixture distributions. The comparative analysis reveals that the approximation yields results in close agreement with simulation outputs, with particularly high accuracy observed for Gaussian mixture cases.
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    Öğe
    Enhancing Control: Unveiling the Performance of Poisson EWMA Charts through Simulation with Poisson Mixture DATA
    (Mdpi, 2023) Uncu, Nusin; Koyuncu, Melik
    Poisson-Exponentially Weighted Moving Average (PEWMA) charts are one of the most frequently used control charts for monitoring count data. But as real-world data often shows overdispersion-prevalent in manufacturing, health care, economics, and marketing-the standard Poisson distribution falls short. One of the ways to tackle overdispersion is to use Poisson mixture distributions. Our study examines Average Run Length (ARL) performance in the presence of Poisson mixture distribution in the PEWMA control charts. Through meticulously designed experiments, we explore different control parameter combinations and employ simulation to evaluate the process. Our graphs illustrate the performance of the PEWMA control chart, offering desired in-control ARL across parameter combinations. Finally, the performance of the PEWMA control chart is presented for the real process data of fastener production.
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    Öğe
    Finite Mixture Model-Based Analysis of Yarn Quality Parameters
    (MDPI, 2025) Karakas, Esra; Koyuncu, Melik; Ukelge, Mulayim Ongun
    This study investigates the applicability of finite mixture models (FMMs) for accurately modeling yarn quality parameters in 28/1 Ne ring-spun polyester/viscose yarns, focusing on both yarn imperfections and mechanical properties. The research addresses the need for advanced statistical modeling techniques to better capture the inherent heterogeneity in textile production data. To this end, the Poisson mixture model is employed to represent count-based defects, such as thin places, thick places, and neps, while the gamma mixture model is used to model continuous variables, such as tenacity and elongation. Model parameters are estimated using the expectation-maximization (EM) algorithm, and model selection is guided by the Akaike and Bayesian information criteria (AIC and BIC). The results reveal that thin places are optimally modeled using a two-component Poisson mixture distribution, whereas thick places and neps require three components to reflect their variability. Similarly, a two-component gamma mixture distribution best describes the distributions of tenacity and elongation. These findings highlight the robustness of FMMs in capturing complex distributional patterns in yarn data, demonstrating their potential in enhancing quality assessment and control processes in the textile industry.