Timetable management in universities is a complex task involving multiple constraints such as faculty availability, classroom capacity, course requirements, and student preferences. Traditional scheduling techniques often fail to accommodate uncertainty and flexibility, leading to conflicts and inefficiencies. This research proposes a Fuzzy Logic-Based Decision Support System (DSS) to optimize timetable scheduling by incorporating soft constraints and preference-based decisionmaking. The system models uncertainty through fuzzy sets and applies inference rules to evaluate and rank scheduling alternatives. A prototype is developed using Python with the scikit-fuzzy library, demonstrating how fuzzy inference can effectively handle imprecision in faculty preferences, room suitability, and time slot allocation. The experimental results show that the proposed approach reduces scheduling conflicts, improves satisfaction levels, and provides a more adaptable framework compared to conventional deterministic methods.
Timetable management, fuzzy logic, decision support system, university scheduling, soft constraints, uncertainty modeling, scikit-fuzzy, Python-based analysis, optimization, artificial intelligence