  Course assignment is a wide-spread problem in education and beyond. Often
students have preferences for bundles of course seats or course schedules over
the week, which need to be considered. The problem is a challenging distributed
scheduling task requiring decision support. First-Come First-Served (FCFS) is
simple and the most widely used assignment rule in practice, but it leads to
inefficient outcomes and envy in the allocation. Recent theoretical results
suggest alternatives with attractive economic and computational properties.
Bundled Probabilistic Serial (BPS) is a randomized mechanism satisfying ordinal
efficiency, envy-freeness, and weak strategy-proofness. This mechanism also
runs in polynomial time, which is important for the large problem instances in
the field. We report empirical results from a first implementation of BPS at
the Technical University of Munich, which allows us to provide important
empirical metrics such as the size of the resulting matching, the average rank,
the profile, and the popularity of the assignments. These metrics were central
for the adoption of BPS. In particular, we compare these metrics to Random
Serial Dictatorship with bundle bids (BRSD). The BRSD mechanism is used to
simulate the wide-spread First-Come First-Served (FCFS) mechanism and it allows
us to compare FCFS (BRSD) and BPS. While theoretically appealing, preference
elicitation is a major challenge when considering preferences over
exponentially many packages. We introduce tools to elicit preferences which
reduce the number of parameters a student needs to a manageable set. The
approach together with BPS yields a computationally effective tool to solve
course assignment problems with thousands of students, and possibly provides an
approach for other distributed scheduling tasks in organizations.
