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Altunoğlu, Burcu

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Burcu

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Burcu ALTUNOĞLU

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    Flight Delay Prediction with Airport Traffic Density Data from an Aviation Risk Management Perspective
    (2025-06-28) Altunoğlu; Altunoğlu, Burcu; Akinet, Mert
    Flight delays are significantly important in risk management for the aviation industry, impacting airline operations, passenger satisfaction, and air traffic management. While existing studies primarily focus on weather-related factors in flight delay prediction, this study explores the influence of airport traffic density on delays from an aviation risk management perspective. Using data mining techniques, the study integrates airport traffic and en-route delay datasets from EUROCONTROL to develop predictive models for delay estimation. The methodology follows a structured approach, including data preprocessing, feature engineering, clustering, and predictive modeling using the Random Forest algorithm. The findings indicate that airport traffic density is a critical predictor of delays, alongside seasonal and regional factors. Regression analysis highlights a strong correlation between congestion levels and delay severity, particularly in peak travel periods. The clustering results reveal four distinct delay patterns, reflecting variations in operational disruptions due to equipment failures and adverse weather conditions. The Random Forest model demonstrates high predictive accuracy, with low error rates confirming its robustness for delay estimation. This study contributes to aviation risk management by providing data-driven insights into flight delays and offering strategic decision-making tools for airline and airport operators. The results emphasize the need for proactive delay mitigation strategies, such as improved airspace allocation and enhanced maintenance processes. Future research could extend this approach by incorporating additional delay factors, such as incident-related disruptions, to further enhance predictive capabilities. By integrating operational data and advanced analytics, this study presents a novel framework for improving delay forecasting and optimizing flight operations.
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    Multi-objective location-distribution optimization in blood supply chain: an application in Turkiye
    (2024-11-15) Altunoğlu, Burcu; Batur Sir, Gül Didem
    Purpose Blood donors are crucial in maintaining the blood supply chain. This study aims to improve the location and distribution of blood donation centers by focusing on two main objectives: minimizing costs and maximizing quality. Minimizing costs includes setting up and transporting blood efficiently while maximizing quality to ensure that blood products are delivered to hospitals promptly and in the right quantities. Methods A multi-objective mathematical model is proposed to address the placement of both fixed and mobile blood donation centers. The first objective focuses on minimizing the costs of setting up centers and transporting blood. The second objective aims to maximize quality by ensuring timely deliveries and meeting hospitals’ blood demand. The model utilizes real-world traffic and blood donation data from urban settings to simulate its effectiveness and applicability in practice. The model uses the epsilon constraint method to optimize both objectives simultaneously. Results The model was tested in various scenarios, optimizing cost and quality separately. The algorithm determined the ideal locations for blood donation centers to meet demand by exploring different options. It also accounted for factors that reduce quality, such as delayed deliveries and product returns, and showed that these issues could be minimized. Conclusion This study highlights the need to balance cost and quality when determining the locations of blood donation centers. Using the epsilon constraint method, the model successfully optimized both objectives, offering valuable insights for improving the efficiency and effectiveness of blood donation operations.