Yazar "Ayaz, Halil Ibrahim" seçeneğine göre listele

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    Automation of FMEA for Computer Servers Using Log Data with Grey Relational Analysis
    (IEEE, 2017) Ayaz, Halil Ibrahim; Testik, Murat Caner
    Failure modes and effects analysis (FMEA) is a powerful and proactive quality tool for defining, detecting, and identifying potential failure modes and their effects. However, conventional FMEA process is sometimes difficult to implement due to workload required and subjectivity of the evaluations performed. Hence, automation of this tool can be useful for some application domains to objectively evaluate failures and faster implementations, which is the aim of this study. To automate the process and eliminate the subjectivity, data-based algorithms such as grey relational analysis and association analysis are implemented in the following with an application to computer servers.
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    A fully data-driven FMEA framework for risk assessment on manufacturing processes using a hybrid approach
    (Pergamon-Elsevier Science Ltd, 2023) Ervural, Bilal; Ayaz, Halil Ibrahim
    The Failure Mode and Effect Analysis (FMEA) is a widely used method that effectively identifies and prioritizes potential risks in a given system or process. However, traditional and modified versions of FMEA are often criticized for their subjective assessments, inadequate risk prioritization methods, and lack of consideration of the importance level of risk factors. To address these issues, this study introduces a data-driven FMEA approach. Specifically, the proposed approach utilizes data-driven risk factors to determine objective rankings of failure modes. This study uses the frequency and stability of failures, time and product loss cost due to failure as objective and data-driven risk factors. These factors enable a more precise description of the influence of risk factors on failure modes. The Modified Criteria Ranking Importance with Intra-criteria Correlation (M-CRITIC) method is employed to assign weights to the identified risk factors, which indicates their level of importance in analysis. Additionally, the recently proposed Alternative by Alternative Comparison (ABAC) method is used to derive the risk priorities of failure modes. The effectiveness and applicability of the developed approach are demonstrated through a case study focused on manufacturing process risk analysis in the food industry. Furthermore, this study contributes to the growing trend toward objective risk calculations for FMEA and highlights the importance of using data-driven models for risk analysis.
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    A Hybrid Fuzzy Rule-Based Polyhedral Separation Approach: Medical Diagnosis Application
    (Springer International Publishing Ag, 2022) Ayaz, Halil Ibrahim; Ervural, Bilal
    Discrimination of two linearly inseparable sets using hyperplane(s) is considered one of the most successful classification methods. There is an expanding body of literature that realize the significance of classification of data accurately. Particularly, medical applications of classification provide meaningful early diagnosis results. However, medical datasets in real-world applications possess some noise and uncertainty. Therefore, dealing with uncertainty is a critical factor for accurate diagnosis. To overcome mentioned drawbacks, this study presents a hybrid fuzzy rule-based robust linear programming (RLP) and h-polyhedral separation (h-PolSep) approaches for breast cancer diagnosis through several consequent stages. In the proposed two-stage model, firstly the data is relabeled according to the fuzzy rule-based system, then the new outputs and original input values are classified using RLP and h-PolSep methods. Input fuzzification, generating membership functions, extracting fuzzy rules, and output defuzzification are examined in detail. A frequently used real-world medical dataset from the UCI Repository: the Wisconsin breast cancer is employed to evaluate the effectiveness of the classification using a number of metrics. Finally, the results demonstrate that the proposed approach effectively handles medical data classification problems.
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    A mathematical model and a heuristic approach for train seat scheduling to minimize dwell time
    (Pergamon-Elsevier Science Ltd, 2021) Ayaz, Halil Ibrahim; Ozturk, Zehra Kamisli
    Rail is fast becoming a key instrument in the transportation of passengers and cargo. Especially, high-speed trains are gaining more importance in recent decades to passenger transportation. However, some problem arises for passenger transportation due to passengers' improper scheduling or trains' dwell time. These problems negatively affect customer satisfaction and revenue management. Although there are many studies about customer satisfaction and revenue management for transportation, a small part of these studies are illustrated in railways. In this study, the train seat scheduling problem is converted into a parallel machine scheduling problem and the problem is redefined considering the scheduling perspective. Then, a mathematical model and a heuristic algorithm are developed considering the mentioned problems. The proposed algorithm provides a feasible scheduling plan in a reasonable time scale, considering dwell times and a proper scheduling plan. Both methods are used to solve the problem, and comparative results are presented.