<![CDATA[University course timetabling is commonly evaluated through operational feasibility, while pedagogical considerations such as cognitive-demand distribution and class timing receive less explicit attention in optimization models. This study developed a spreadsheet-based Integer Linear Programming (ILP) model that integrates conventional hard constraints with pedagogically informed soft constraints to generate a feasible and more educationally responsive timetable. A mathematical modeling and computational simulation design was employed using synthetic data comprising five courses, four lecturers, two classrooms, five daily time slots, and two instructional days. The model incorporated classroom allocation, lecturer assignment and availability, room suitability and capacity, required course duration, and day-room consistency as hard constraints, while cognitive-demand distribution, post-lunch scheduling, and first-slot placement were treated as soft constraints. The formulation was implemented in Microsoft Excel and solved using OpenSolver with the COIN-OR CBC engine. The resulting timetable satisfied all hard constraints, confirming operational feasibility, while the three pedagogical penalty components produced values of 4, 0, and 1, respectively. These results indicate that excessive daily concentration of cognitively demanding courses could be fully avoided, although some temporal-placement penalties remained under the available scheduling conditions. The findings demonstrate that pedagogically informed preferences can be incorporated into an accessible ILP framework without compromising feasibility. This study therefore provides a transparent proof of concept for extending university timetabling beyond conflict avoidance toward multidimensional schedule quality that combines operational requirements with explicit cognitive and temporal considerations. The approach also preserves transparency for institutional inspection and adaptation.]]>
