Fibonacci heaps and their uses in improved network optimization algorithms
Journal of the ACM (JACM)
The stable marriage problem: structure and algorithms
The stable marriage problem: structure and algorithms
When is the classroom assignment problem hard?
Operations Research - Supplement
Approximation algorithms for NP-hard problems
Approximation algorithms for NP-hard problems
Competitive analysis of network load balancing
Journal of Parallel and Distributed Computing
“The quickest transshipment problem”
Proceedings of the sixth annual ACM-SIAM symposium on Discrete algorithms
Centralized and distributed algorithms for network scheduling
Centralized and distributed algorithms for network scheduling
A Constraint Programming Approach to Extract the Maximum Number of Non-Overlapping Test Forms
Computational Optimization and Applications
Hierarchical timetable construction
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
University space planning and space-type profiles
Journal of Scheduling
The classroom assignment problem: Complexity, size reduction and heuristics
Applied Soft Computing
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We consider the problem of scheduling a set of classes to classrooms with the objective of minimizing the number of classrooms used. The major constraint that we must obey is that no two classes can be assigned to the same classroom at the same time on the same day of the week. We present an algorithm that produces a nearly optimal schedule for an arbitrary set of classes. The algorithm's first stage produces a packing of classes using a combination of a greedy algorithm and a non-bipartite matching and the second stage consists of a bipartite matching.First we show that for one variant of the problem our algorithm produces schedules that require a number of classrooms that is always within a small additive constant of optimal. Then we show that for an interesting variant of the problem the same algorithm produces schedules that require a small constant factor more classrooms than optimal. Finally, we report on experimental results of our algorithm using actual data and also show how to create schedules with other desirable characteristics.