Modelling and Solving Multi-objective University Course Timetabling Problems

University module timetabling involves organising hundreds of staff and thousands of students whilst respecting complex scheduling constraints. Beyond resource allocation, this process requires balancing competing stakeholder preferences: students seeking convenient schedules, staff requiring flexibility, and administrators pursuing operational efficiency. This makes university module timetabling an inherently multi-objective, multi-stakeholder optimisation problem. The current literature inadequately addresses strategic decision-making and the emerging trend of hybrid teaching, in which students participate in activities both online and in-person. This thesis develops a novel integer programming model that incorporates hybrid teaching modes and proposes solution methods to find trade-offs between objectives. We introduce a student partitioning approach based on module requests that supports bound computation for objective functions and guides neighbourhood selection in our proposed matheuristic algorithm. This algorithm finds both lexicographic solutions that respect stakeholder priorities and Pareto frontier approximations that reveal objective trade-offs. Our framework transforms timetabling from purely operational scheduling into strategic decision support, enabling universities to evaluate policy alternatives and balance competing interests. We demonstrate practical applicability using benchmark instances and real-world data from a UK university, showing how multi-objective analysis can inform timetabling decisions.