An Adaptive Fuzzy Ant Colony Optimization-Based System for Scheduling University Lecture Timetable

Abstract

 University lecture timetabling is a complex optimization problem involving multiple constraints and resource limitations. This study presents a hybridized Ant Colony Optimization (ACO) and Fuzzy Logic (FL) based system to enhance scheduling efficiency and accuracy. By integrating Constraints Logic Programming, the proposed system effectively handles both hard and soft constraints, optimizing lecture schedules while minimizing clashes and resource conflicts. FL is employed to manage uncertainties in the search space, improving the adaptability and decision-making capabilities of ACO. The study also incorporates a knowledge base to store and process timetabling constraints, ensuring logical and structured allocations. Scatter plot analysis reveals that certain hard constraints, such that no lectures should be scheduled after 6:00 pm (H3) and the course must be assigned to either the first or second semester (H4) exhibit high stability and significantly influence timetable optimization. The system is implemented using MATLAB R2015a, Microsoft SQL Server, and Python, with results demonstrating improved scheduling efficiency compared to conventional methods. The proposed model enhances knowledge assimilation, reduces lecture delays, and contributes to more effective university timetabling solutions.