Introduktion til Rasmus & Dennis​

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Quality timetable recovery

Industrial PhD project by Rasmus Ø. Mikkelsen
Timetable quality can mean the difference between effcient and wasteful use of resources, and as such, good timetables are very important for all academic institutions. Today, most timetables are produced under the assumption of a static environment and does not account for inevitable disruptions. For any academic institution, a large number of changes happen during a semester and are handled manually. The objective of this project is to conduct research in quality recovery of disrupted timetables using advanced Operations Research (OR) methods; enabling better dynamic timetables. The end goal is the development of decision supports tools for timetable administrators, to enable them to save administrative time and create better timetables for students and educators.

Disrupted timetables at educational institutions can be recovered using specialized mathematical models, thus improving utilization of resources and maintaining teaching quality.

Strategic university timetabling

PhD project by Dennis S. Holm
The objective of the project is to conduct research in timetabling as a tool for strategic decision making at universities. Timetabling problems have been researched for decades, especially the tactical timetabling problem, which deals with the allocation of timeslots and rooms for a number of events where the goal is to schedule assignments. However, the strategic timetabling problem is a newly discovered subfield. The strategic timetabling problem is dealing with the efficient allocation of overall resources, e.g. building stock and teaching periods, where the goal is to control quality and costs. Furthermore, the project aims to research in decision supporting algorithms that can help the university management in utilizing the resources more efficiently. In addition, it should be possible to do what-if analyses on which resources are needed in the future.

Resource requirements of a university timetable can be mathematically modelled, thus enabling resource utilization to be improved using Operations Research methods.