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    A Fuzzy AHP-TOPSIS Approach for Dynamic Due Date Assignment in Machine Scheduling
    (Springer International Publishing Ag, 2025) Duzgit, Zehra; Ozcan, Zuhal; Yavuz, Tonguc
    Dynamic machine scheduling involves integrating new jobs into existing plans with minimal disruption. Quoting due dates for new jobs presents a trade-off between customer satisfaction and tardiness risk. The complexity increases in family setups, where batch processing requires additional setups. This study focuses on due date assignment for new arrivals and scheduling of both new and incomplete jobs. Four objectives are considered: minimizing total setup time, tardiness of incomplete jobs, due date of the new job, and energy consumption. To address uncertainties in processing and setup times, a hybrid fuzzy multicriteria decision-making (MCDM) approach combining Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) is proposed. FAHP captures expert judgments to weigh objectives, while FTOPSIS ranks insertion positions under uncertainty. The framework offers a computationally efficient, expert-driven alternative to optimization models, suitable for real-time dynamic scheduling.
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    A Fuzzy Multi-criteria Decision-Making Approach for Course Scheduling in Higher Education
    (Springer International Publishing Ag, 2025) Yavuz, Tonguc; Ozcan, Zuhal
    Course scheduling in higher education requires balancing the diverse preferences of students, instructors, and administrators under various logistical constraints. To address the inherent uncertainty in these preferences, this study proposes a fuzzy multi-criteria decision-making (MCDM) framework incorporating four methods: Fuzzy Analytic Hierarchy Process (FAHP), Best-Worst Method (BWM), Fuzzy TOPSIS, and Fuzzy Ordered Weighted Averaging (Fuzzy OWA). A case study involving evaluations from all stakeholder groups is conducted to assess scheduling alternatives based on criteria such as session timing, instructor preferences, classroom suitability, and fairness. The results reveal consistent rankings across methods, with each technique offering unique interpretive advantages-hierarchical structuring (FAHP), simplicity (BWM), intuitive ranking (TOPSIS), and attitudinal flexibility (OWA). This research highlights the potential of fuzzy MCDM approaches to support equitable and robust scheduling decisions under uncertainty.
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    A Theoretical Analysis of Fuzzy MCDM Methods for Healthcare Resource Allocation Under Uncertainty
    (Springer International Publishing Ag, 2025) Ozcan, Zuhal; Yavuz, Tonguc
    Healthcare systems face critical challenges in resource allocation and decision-making under uncertainty. This study applies three fuzzy multi-criteria decision-making (MCDM) methods-Fuzzy AHP, Fuzzy TOPSIS, and Fuzzy OWA-to address these challenges. Each method is mathematically analyzed and applied to a case study involving the allocation of limited medical resources across patient groups, evaluated by severity of condition, treatment cost, and expected benefit. The results show that Fuzzy AHP supports hierarchical decisions but is computationally intensive; Fuzzy TOPSIS provides intuitive rankings but is sensitive to weight accuracy; and Fuzzy OWA models risk preferences flexibly but requires careful aggregation weighting. Comparative analysis highlights the methods' complementary strengths and suitability for healthcare contexts. The study contributes to the development of effective decision-support systems under uncertainty. Future directions include hybrid approaches and integration with real-time data for broader applicability.