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University rostering is a resource-allocation problem, not just a matter of putting classes on a calendar. Planners must fit student demand, staff availability, course structures, teaching modes, rooms and travel time together—while deciding which competing goals matter most. Hybrid teaching adds another decision: whether students attend online or in person, and whether the room and class can support both.
What university rostering involves
In research on university course timetabling, the core task is to assign people to teaching events and assign those events to times and locations, subject to constraints. A workable timetable must meet demand and be feasible for students, staff and the institution’s spaces.
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Two common approaches use different starting points. Curriculum-based timetabling schedules events grouped into fixed curricula. Post-enrolment-based timetabling uses student demand or enrolment information. Real planning may need to account for both: programme structures define required teaching, while students’ choices shape which events they need to attend.
Terminology varies by institution. In UK usage, “course” may mean an entire programme of study made up of modules; the hybrid-timetabling study by Matthew Davison, Ahmed Kheiri and Konstantinos G. Zografos uses “module” for the component taught units. Planners should define these terms locally before comparing data or configuring a scheduling model.
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Why a university timetable is hard to build
Demand and course structures
Students choose modules, and their choices affect class sizes and which combinations of events they must attend. Those sizes can be difficult to predict, especially when enrolment or preferences change. A schedule that fits the formal programme structure can still fail students if the available class times conflict with the modules they want or need.
Staff, rooms and teaching requirements
Events need staff who are available at the assigned time and spaces suitable for the activity. Room capacity is only one part of that fit: a specialist class may require particular equipment, a certain layout or setup time. A room can be large enough yet still be unsuitable, or unavailable when the class needs it.
Conflicts and movement around campus
Students and staff should not be assigned to overlapping events. For in-person teaching, the timetable also has to allow enough time to travel between locations. This matters on dispersed campuses and across multiple sites: two back-to-back classes can be conflict-free on paper but impossible to reach in practice.
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Institutional rules differ
A scheduling model cannot be assumed to encode every local rule. Davison, Kheiri and Zografos note that their model does not include all distribution constraints found in the ITC-2019 formulation—for example, rules requiring selected classes to take place on different days or in the same room. A model therefore needs to reflect the institution’s actual requirements rather than being treated as a universal timetable generator.
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How hybrid teaching changes the scheduling problem
With hybrid provision, planners may need to decide not only when and where a class happens, but whether it runs online, in person or in a format that supports both. They may also need to assign students to classes and account for each student’s preferred participation mode. Not every room is equipped or appropriate for hybrid delivery, so room availability and mode availability are linked.
Mode affects feasibility as well as preference. Students attending online do not need travel time between campus buildings; those attending in person do. A room’s physical capacity and a class’s pedagogical size limit still matter even when some students join remotely. An assignment that is possible for a remote participant may not be workable for someone expected in the room.
The hybrid model explicitly balances three objectives:
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- Reduce scheduling problems: minimize issues such as clashes and inadequate time to travel between physical locations.
- Respect mode preference: minimize cases where students attend in a mode other than the one they prefer.
These goals can conflict. A timetable that serves more requests may leave more students with a non-preferred mode; prioritizing preferred modes may make it harder to fit demand or avoid conflicts. The institution has to decide how to weigh those outcomes—there is no single objective that is right for every campus.
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How to compare timetable options
Before choosing between schedules, planners should make their priorities explicit. The first three criteria below are objectives in the published hybrid model. The remaining ones are practical comparison criteria; a 2026 room-allocation paper’s search-result abstract describes related room-allocation goals, but its full article was not available for confirmation.
| Criterion | Question to ask |
|---|---|
| Demand served | How many requested or required modules can students take? |
| Feasibility and friction | Are there student or staff overlaps, or too little time to travel between physical classes? |
| Mode fit | How often must students participate in a mode they did not prefer? |
| Room fit and capacity | Does each room meet the event’s capacity, equipment, layout and pedagogical requirements? |
| Campus movement and continuity | Does the schedule create difficult building changes or unnecessary room changes? |
| Operational resilience | If disruption forces a change, can the revised schedule still work and remain sufficiently close to the original? |
These criteria make trade-offs visible instead of hiding them inside a single score. For example, a room assignment can look efficient by capacity yet be a poor fit for equipment needs; a schedule can avoid conflicts but still impose a less-preferred mode on many students. The right comparison depends on which outcomes the institution is prepared to prioritize.
Plan across the timetable lifecycle
Timetabling begins before individual classes are assigned and continues after schedules are published. Michael Lindahl’s 2017 doctoral thesis at DTU frames the work across room planning, teaching periods, course scheduling and recovery after disruption. That suggests three connected planning horizons:
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Strategic: decide what resources exist
Choices about rooms and teaching periods determine the resources later available to planners. If the available spaces do not match the mix of teaching activities, no timetable can fully solve that mismatch.
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Tactical: assign classes and resources
Course and room assignments turn student demand, staffing and space availability into a schedule. At this stage, the model’s constraints and priorities need to match institutional practice.
Operational: adjust when circumstances change
Disruptions can make a published schedule unworkable. Recovery planning asks whether a revised timetable can restore feasibility without creating excessive change for students and staff. Treating recovery as part of the problem, rather than an afterthought, makes resilience a deliberate criterion.
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Hybrid timetabling research
Davison, Kheiri and Zografos published a peer-reviewed hybrid university timetabling article online in 2024; it appeared in volume 28 of the Journal of Scheduling in 2025. Their experiments use benchmark instances adapted from ITC-2019, with explicit assumptions about room capacity, student preferences and which rooms support hybrid teaching. The authors’ abstract says: “The results of these experiments demonstrate that the model can be used to find demand-driven schedules for universities that include hybrid teaching.” This demonstrates the model on benchmark data; it is not evidence of adoption at a particular university or a measured outcome from a live campus.
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A 2022 INFORMS paper by Dimitris Bertsimas, Arthur Delarue and Julia Yan reported two pandemic-era institutional scenarios. In an MIT planning scenario that shifted from the usual two-semester calendar to three semesters, with each student attending two semesters in person and without increasing faculty workloads, the model projected that more than 90% of student course demand could be satisfied on campus. In a separate fall 2020 schedule, two thirds of Sloan students were offered the opportunity for in-person learning in at least half their courses despite a fourfold reduction in classroom capacity. These are results from specific historical cases, not general performance guarantees for other universities.
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A provisional room-allocation example
A search-result abstract for a 2026 Procedia Computer Science room-allocation paper describes a semester dataset with approximately 10,000 students, more than 26,000 class sessions, over 120 classrooms and more than 20 room types. The full article was not available to confirm those figures or its precise methods, so they should be treated as provisional rather than as a verified benchmark for campus scheduling.
What planners should define before optimizing
An optimization result is only useful if the inputs and priorities represent the campus. Before using a model or comparing proposed schedules, establish:
- Which events and student demand data must be scheduled, and how local terms such as “course” and “module” are defined.
- Which room attributes are mandatory for each type of teaching, including capacity, equipment, layout and hybrid capability.
- How staff and student conflicts, travel time and setup time are represented.
- Whether mode preference is a hard requirement, a priority to satisfy when possible, or one objective balanced against demand and schedule feasibility.
- Which local distribution rules apply, and how disruptions and later schedule changes will be handled.
Making these choices explicit helps administrators, estates teams and timetable planners understand what a proposed schedule is optimizing—and what it is willing to trade away.
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