University rostering is a resource-allocation problem, not simply a matter of placing classes in empty calendar slots. A workable timetable must fit student demand, course structures, staff availability, suitable rooms, travel between locations and teaching mode—while balancing goals that can conflict.
Hybrid teaching adds another decision: whether a student attends a class in person, online or through a session designed to support both. There is no single timetable that optimizes every campus’s priorities; institutions need to define their constraints and decide which outcomes matter most.
What does university rostering involve?
In the timetabling literature, the university course timetabling problem involves assigning people to events and assigning those events to times and locations, subject to constraints. In practice, a class has to be scheduled when its students and staff can attend, in a space that can support the teaching, without making the rest of their schedules unworkable.
Two common approaches emphasize different information. Curriculum-based timetabling schedules events grouped into fixed curricula. Post-enrolment-based timetabling uses student demand or enrollment data. A real planning process may need to account for both: the structure of programmes and modules, as well as which students want or need each module.
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Terminology varies by country and institution. In UK usage, a “course” can mean a whole programme of study made up of modules; the hybrid-timetabling paper discussed here uses “module” for the component taught units. Planners should define what “course,” “module,” “class” and “event” mean in their local system before translating rules into a timetable model.
Why is building a university timetable so difficult?
Student choice makes demand hard to fit
Students may choose from a range of modules, while programme structures still impose requirements. Their selections shape class sizes and create individual timetable conflicts: a schedule that works for a programme as a whole may still prevent particular students from taking the combination they need.
Staff, time slots and travel constrain one another
Staff and students cannot attend overlapping events. Nor is a sequence automatically feasible just because its classes do not overlap: a student or instructor may need enough time to travel between buildings. Dispersed campuses and multiple sites make the gap between sessions a practical constraint, not just an administrative preference.
Rooms must suit the teaching, not merely be vacant
Room capacity is only one part of the match. A chemistry class may require specialist equipment and setup time; other teaching may depend on a room’s layout or facilities. Class-size rules may also set a limit below the room’s physical capacity. A room that is free at the right hour can still be unsuitable for the event.
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Hybrid delivery adds room and participation choices
For hybrid teaching, planners may need to decide whether a room can support the required technology, which students attend which class, and whether each student participates online or in person. Not every room is equipped for hybrid delivery. Students may also have different mode preferences, and only in-person attendance entails travel to campus.
These decisions are connected. Assigning a student to an in-person class affects room capacity and travel; assigning that student online may better fit their preference but still has to work with how the class is delivered. A schedule can therefore be feasible on paper yet fail to meet the teaching or participation requirements that motivated it.
What should a hybrid timetable optimize?
Davison, Kheiri and Zografos’s hybrid-timetabling model makes three objectives explicit: maximize the number of module requests met, minimize scheduling issues, and minimize cases in which students attend in a mode other than their preference. These goals can compete. For example, a schedule that serves more requests may leave fewer preferred-mode assignments or require a less convenient arrangement.
Institutions should select and prioritize objectives according to their own teaching model and policies. Useful comparison criteria include:
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- Demand served: how many requested or required modules students can take.
- Schedule feasibility: whether student and staff conflicts are avoided and there is enough time to move between physical locations.
- Mode fit: how often students can participate in their preferred online or in-person mode.
- Room fit and capacity: whether space, equipment, layout and pedagogical class-size limits suit the event.
- Campus movement and continuity: whether the schedule requires workable travel and avoids unnecessary room changes.
- Resilience: whether a schedule can be adjusted after disruption while remaining workable and, where useful, close to the original.
The first three objectives appear explicitly in the hybrid model. Room relocation, room-type mismatch, capacity waste and room changes are additional criteria described in the abstract of a 2026 room-allocation paper; without its full text, those details should be treated as provisional rather than as verified findings about a particular university.
How should planners think about the work over time?
Rostering begins before classes are assigned and continues after the timetable is published. Michael Lindahl’s 2017 DTU doctoral thesis describes room planning and the teaching periods that determine available resources, as well as recovery after disruption. That suggests three connected planning horizons:
Strategic: decide what resources and teaching periods exist
Room inventory, room types and the available teaching periods determine what the scheduling process can use. A timetable cannot resolve a shortage of suitable specialist or hybrid-capable spaces simply by rearranging events; resource decisions shape the feasible options from the start.
Tactical: assign classes, people, times and places
At this stage, planners fit student demand and course structures to staff availability, time slots, room suitability and capacity, mode choices and travel constraints. A mathematical model can help compare schedules, but only if its constraints reflect the institution’s real rules.
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Operational: adjust when circumstances change
Changes after publication may require a room, time or mode adjustment. The revised timetable must still satisfy the constraints that remain in force, and planners may also want to limit disruption to students and staff. Treating recovery as part of the design makes resilience a planning criterion rather than an afterthought.
Why is a model not a universal timetable recipe?
The hybrid model’s experiments use benchmark instances adapted from ITC-2019 and make assumptions about room capacity, student preferences and which rooms support hybrid teaching. The authors write: “The results of these experiments demonstrate that the model can be used to find demand-driven schedules for universities that include hybrid teaching.” This is a result demonstrated on benchmark data; it is not evidence that a particular university deployed the model or achieved the same outcomes on campus.
The model also does not include every rule that a university may need. For example, it omits some distribution constraints present in the ITC-2019 formulation, such as requirements for selected classes to occur on different days or in the same room. Institutions therefore need to configure or extend any model to reflect their own course rules, room policies and priorities rather than assume an off-the-shelf formulation covers them all.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What does published evidence say about capacity pressure?
Two pandemic-era results illustrate how optimization can help planners explore choices under severe capacity limits. Both are specific to MIT and Sloan School of Management scenarios, not general performance guarantees for universities.
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| Published result | What it describes | How to interpret it |
|---|---|---|
| More than 90% of student course demand could be satisfied on campus | 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. | A projection from a pandemic planning model, reported by Dimitris Bertsimas, Arthur Delarue and Julia Yan in an INFORMS article published online in November 2021 and in a 2022 journal issue. It is not a general guarantee for other institutions. |
| Two thirds of Sloan students were offered in-person learning in at least half their courses | A schedule implemented for fall 2020 despite a fourfold reduction in classroom capacity. | A specific pandemic-era institutional case reported by Bertsimas, Delarue and Yan in the same INFORMS publication; it does not establish what another university could achieve. |
A separate 2026 room-allocation article’s search-result abstract reports a semester dataset of approximately 10,000 students, more than 26,000 class sessions, over 120 classrooms and more than 20 room types. Because the full article is not available here, these dataset figures and the abstract’s stated objectives should not be treated as independently confirmed evidence or as a benchmark for other campuses.
How can an institution judge whether a schedule is workable?
A timetable should be evaluated against a stated set of institutional priorities, not just whether every event has a room and a time. A practical review can ask:
- Which required or requested modules can students actually take, and which demands remain unsatisfied?
- Are staff and student conflicts avoided, and are travel gaps adequate for the campus layout?
- Do room capacity, equipment, setup needs and class-size rules match each event?
- Which rooms can genuinely support the planned hybrid delivery?
- How often does a student’s assigned participation mode differ from their preference?
- Can planners respond to a disruption without creating new conflicts or excessive changes?
These checks make trade-offs visible to administrators, timetable planners and estates teams. A schedule that improves one metric may worsen another; the important step is to decide openly which constraints are non-negotiable and which outcomes can be balanced.
Why does the timetable matter beyond administration?
Michael Lindahl’s 2017 DTU thesis captures the human consequence: “A timetable has many stakeholders, and it is the task of planners to accommodate their needs as far as possible, as it has a significant influence on the daily life of both staff and students.” In a hybrid campus, those stakeholders also experience the consequences of mode, travel and room choices. The timetable is therefore part of how the institution delivers its teaching, not merely a back-office calendar.
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