Evaluate the performance of university timetabling problem with various artificial intelligence techniques

Hooi, Charmaine Wai Yee (2025) Evaluate the performance of university timetabling problem with various artificial intelligence techniques. Final Year Project, UTAR.

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Abstract

University timetabling is a complex and critical task in higher education institutions as it involves the assignment of courses, lecturers, and students to available timeslots and venues while satisfying various constraints. The project focuses on developing an automated university course timetable scheduling tool using Genetic Algorithm (GA). University course timetable scheduling (UCTTP) is a well-known optimization problem due to its NP-hard nature and the complexity of the problem increases exponentially with eh addition of constraints. Over time, numerous algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and other approaches have been introduced to address the challenges of optimizing class schedules. While each university or institution have its own unique constraints, this project aims to improve existing timetabling systems by introducing a new constraint, the ‘Proximity and Travel Minimization Constraint’ which optimizes class schedules to minimize travel distances between venues scheduled in adjacent time slots. By implementing this new constraint, the project addresses the gap in traditional timetabling methods, which often overlook the impact of travel distances on the efficiency and experience of both lecturers and students. Hence, through the application of GA, this project aims to develop an efficient university class timetabling tool that integrates the newly introduced constraint.

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