Browsing by Author "Mizrak, Filiz"

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Applying Entropy Weighting and 2-Tuple Linguistic T-Spherical Fuzzy MCDM: A Case Study of Developing a Strategic Sustainability Plan for Istanbul Airport
(MDPI, 2024) Mizrak, Filiz; Polat, Levent; Tasar, Sezin Acik
This study presents a novel sustainability plan tailored for Istanbul Airport, leveraging advanced decision-making methodologies to address the urgent need for sustainable practices in aviation. By integrating the entropy weighting and 2-tuple linguistic T-spherical fuzzy multi-criteria decision-making (MCDM) models, the study offers a comprehensive approach to evaluating and prioritizing sustainability criteria based on expert input from 12 professionals. The novelty of this research lies in its unique combination of advanced MCDM techniques with cutting-edge technologies, including IoT-enabled monitoring systems, digital twin models, blockchain-based sustainability reporting, and carbon capture initiatives, tailored specifically for large-scale airport operations. The study develops a phased implementation roadmap comprising three stages: (1) a short-term focus on energy efficiency and renewable energy infrastructure, achieving significant cost reductions within a 3-7.5-year payback period; (2) medium-term initiatives integrating IoT and digital twins to enhance operational efficiency; and (3) long-term measures incorporating carbon capture and blockchain for transparency and compliance. Key implementation steps include upgrading energy systems, deploying IoT sensors, creating digital replicas of airport infrastructure, and establishing regulatory and stakeholder collaboration frameworks. This research contributes a replicable framework for airports worldwide, bridging theoretical models with actionable solutions.
Developing a Strategic Framework for Airline Destination Selection: A Multi-Criteria Decision-Making Approach Applied To Turkish Airlines
(Elsevier, 2025) Mizrak, Filiz; Mizrak, Kagan Cenk; Akkartal, Gonca Reyhan
The purpose of this study is to develop a comprehensive decision-making framework that airlines can use to strategically select new destinations, demonstrated here with a case application for Turkish Airlines. Addressing the complexities of route expansion, the study integrates the Entropy weighting method with the 2-Tuple Linguistic T-Spherical Fuzzy Decision by Opinion Score Method (2TLTS-FDOSM) to evaluate potential destinations across critical criteria, including market demand, economic impact, and regulatory environment. Data were collected from industry reports, expert evaluations, and publicly available aviation metrics to support a thorough and adaptable analysis. Findings from the Turkish Airlines case reveal San Antonio, TX (SAT), Portland, OR (PDX), and Nashville, TN (BNA) as top-ranked destinations, each offering distinct economic and operational benefits. This study contributes to aviation management literature by presenting a versatile, data-driven framework that can guide route selection for carriers in competitive markets. The framework's ability to balance quantitative and qualitative insights underscores its potential for broader application in network planning, promoting sustainable growth within the aviation industry.
Digital Detox: Exploring the Impact of Cybersecurity Fatigue on Employee Productivity and Mental Health
(Springer Nature, 2025) Mizrak, Filiz; Demirel, Hatice Gokce; Yasar, Okan; Karakaya, Turhan
This study investigates the growing phenomenon of cybersecurity fatigue and its implications for employee productivity and mental health in the high-demand sectors of information technology (IT), finance, healthcare and education. Utilizing a quantitative research methodology, the study surveyed 351 employees from these industries to analyze the relationships between cybersecurity fatigue, work efficiency, and mental health indicators, including stress and anxiety. The findings highlight cybersecurity fatigue as a significant factor contributing to burnout, reduced productivity, and increased psychological strain. Structural Equation Modeling (SEM) demonstrates the moderating effects of digital detox initiatives and mental health support strategies in mitigating fatigue and improving employee well-being and organizational performance. This research addresses a critical gap by focusing on the human dimensions of cybersecurity management and offers practical recommendations for simplifying protocols and fostering resilience. The study provides actionable insights for organizations operating under stringent cybersecurity requirements, enabling them to enhance employee satisfaction and performance.
Investment Strategies for Renewable Energy Technologies and Harvesting Systems in Airport Operations Using Spherical Fuzzy MCDM Models
(Nature Portfolio, 2025) Mizrak, Filiz; Sahin, Didem Rodoplu
This study presents a novel evaluation framework for prioritizing investment strategies in sustainable airport energy systems by integrating advanced fuzzy decision-making techniques with artificial intelligence-based expert weighting. Specifically, it employs a hybrid Spherical Fuzzy CRITIC-RATGOS model to rank renewable energy alternatives based on economic feasibility, environmental impact, technological efficiency, scalability, and operational reliability. To address limitations associated with equal expert weighting, a Principal Component Analysis-driven dimension reduction technique is applied to calibrate expert influence based on professional background and consistency of evaluation. The model is applied to a real-world case study at Istanbul Airport, demonstrating that AI-optimized energy management, solar microgrids, and waste-to-energy conversion are the most promising investment alternatives. In contrast, although technologies such as piezoelectric harvesting show future potential, their current limitations reduce their immediate feasibility. Sensitivity analysis affirms the robustness and stability of the results across various weighting configurations. The proposed framework contributes to both theory and practice by offering a scalable, transparent, and replicable decision-support tool for airport authorities, policymakers, and energy planners aiming to align infrastructure development with global sustainability and decarbonization goals.
Prioritizing Cybersecurity Initiatives in Aviation: A DEMATEL-QSFs Methodology
(Cell Press, 2024) Mizrak, Filiz; Akkartal, Gonca Reyhan
The aviation industry's growing dependence on digital technologies necessitates robust cybersecurity measures to counter advanced threats. This study integrates the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method with Quantum Spherical Fuzzy Sets (QSFS) to enable precise and reliable decision-making under uncertainty. Key criteria, identified through expert evaluations, include Threat Detection Systems (TDS), Data Encryption Protocols (DEP), Regulatory Compliance (RC), Incident Response Plans (IRP), User Training (UT), Access Control Mechanisms (ACM), and Network Security Solutions (NSS). Analysis using the proposed method revealed that "Regulatory Compliance" and "Threat Detection Systems" are the most influential factors, emphasizing the need for strict adherence to standards and advanced threat detection capabilities. Additionally, the significance of "User Training" and "Data Encryption Protocols" underscores the importance of comprehensive training programs and strong encryption measures. By incorporating strategic management theories such as the Resource-Based View (RBV), Contingency Theory, and Risk Management Theory, this study presents a strategic framework to assist aviation organizations, policymakers, and researchers in developing effective cybersecurity strategies, ensuring the safety and security of global air travel.
Strategic Deployment of Piezoelectric Energy Harvesting in Smart Urban Infrastructure: A Hybrid Qpfrs, M-SWARA, K-Means Clustering, and Promethee Evaluation for Sustainable Advantage
(Elsevier, 2025) Mizrak, Filiz; Yasar, Okan
This study develops a hybrid multi-criteria decision-making (MCDM) model to evaluate and prioritize kinetic energy harvesting materials for urban infrastructure, a critical area as cities increasingly adopt sustainable technologies to meet energy demands. The integration of Quadripartitioned Fuzzy Rough Sets (QPFRS), MSWARA, K-Means clustering, and PROMETHEE offers a novel approach for addressing the complexities of material selection by balancing factors such as energy efficiency, durability, cost, scalability, and environmental impact. The strategic importance of this research lies in its potential to guide urban planners and policymakers toward making informed decisions that align with long-term sustainability goals and competitive advantages in smart city development. The findings identify BaTiO3 and ZnO as the most suitable materials for large-scale urban projects, offering superior performance in scalability and environmental sustainability, while materials like PZT exhibit strong energy efficiency but pose environmental concerns. By offering a comprehensive, adaptable evaluation framework, this study contributes both methodologically and practically to the growing field of sustainable urban infrastructure, ensuring that material selection not only meets immediate energy needs but also supports long-term urban development strategies.