Multi-objective university course timetabling produces a Pareto front of trade-off solutions, but ultimately, one timetable must be selected for deployment. Existing studies commonly base this choice on objective values, robustness criteria, or stakeholder preferences, while the downstream recovery implications of selecting different Pareto representatives remain insufficiently understood. This study proposes a deployment-oriented evaluation framework to examine whether the selected Pareto-representative timetable affects recovery after calendar-based disruptions. The framework keeps the original optimization objectives fixed, selects the best-f1, best-f2, and compromise timetables from each run, projects them onto an academic calendar with actual dates and holidays, and evaluates them under five disruption scenarios using greedy direct rescheduling followed by target-meeting fulfillment repair. The framework is evaluated using institutional scheduling and calendar data from the odd and even semesters of the 2025/2026 academic year, which provide a natural contrast in room-slot occupancy and disruption exposure. Weekly timetables are generated using NSGA-II by minimizing a weighted soft-constraint penalty (f1) and average room-capacity waste (f2), with the experiments repeated across five independent random seeds. No representative timetable produced a consistent recovery advantage under the tested setting: differences in the direct rescheduling success rate were small relative to seed-to-seed variation, even when the representative timetables differed substantially in their class placements. The larger contrast occurred between semesters. The denser odd semester, with approximately 91% room-slot occupancy, directly recovered about one-third of affected meetings, whereas the less dense even semester, with approximately 52% occupancy, directly recovered nearly all affected meetings. Both semesters achieved 100% target-meeting fulfillment after repair. Overall, the stronger observed contrast occurred between semester settings and was more consistent with differences in structural slack than with the selected representative category.