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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.
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    Branch-and-price approach for robust parallel machine scheduling with sequence-dependent setup times
    (Elsevier, 2022) Yanikoglu, Ihsan; Yavuz, Tonguc
    This paper studies a machine scheduling problem that minimizes the worst-case total tardiness for unrelated parallel machines with sequence-dependent setup and uncertain processing times. We propose a robust optimization reformulation of the related machine scheduling problem and discuss several important properties of the mathematical model and the reformulation approach. The proposed model generalizes robust parallel machine scheduling problems by including sequence-dependent setup times and ellipsoidal uncertainty sets. Another key contribution of the paper is to show that scheduling problems usually have alternative optimal solutions for the worst-case tardiness objective, whose performance under nominal processing times may vary or vice a versa. This issue has been addressed by studying the Pareto efficient extensions of the proposed robust optimization models to provide solutions that are immune to changes in processing times. A branch-and-price algorithm has been developed to solve realistically sized instances in less than one hour, which a commercial solver cannot achieve. Numerical results show the effectiveness of the proposed approach since realistically sized instances such as (4 machines, 32 jobs) and (150 machines, 300 jobs) can be solved to optimality within the time limit, and the (average) objective function value improvement made by the robust approach can get as high as 56% compared with the (nominal) optimal solutions that ignore uncertainty in problem data.(c) 2021 Elsevier B.V. All rights reserved.
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    Graph-based heuristics for rest difference minimisation
    (Taylor & Francis Ltd, 2025) Atan, Tankut; Yavuz, Tonguc; Cavdaroglu, Burak
    Complaints often arise from teams with less rest time between games in a tournament, highlighting the importance of minimising rest differences for fairness. Achieving fairness through rest difference minimisation is an empirically hard problem when determining the opponents and matchdays of teams in a tournament. To address this issue, we propose novel heuristic approaches based on graph theory. Remarkably, one of our heuristic methods significantly outperforms previously reported outcomes. We also show that the widely used canonical schedule has the maximum optimal rest difference value among all possible opponent schedules, and if the number of rounds is a prime number, shuffling the rounds within the canonical schedule offers no advantages in reducing rest differences. Furthermore, we present an efficient integer programming formulation to determine the total rest difference for a given opponent schedule.