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Course assignment is a wide-spread problem in education and beyond. Oftenstudents have preferences for bundles of course seats or course schedules overthe week, which need to be considered. The problem is a challenging distributedscheduling task requiring decision support. First-Come First-Served (FCFS) issimple and the most widely used assignment rule in practice, but it leads toinefficient outcomes and envy in the allocation. Recent theoretical resultssuggest alternatives with attractive economic and computational properties.Bundled Probabilistic Serial (BPS) is a randomized mechanism satisfying ordinalefficiency, envy-freeness, and weak strategy-proofness. This mechanism alsoruns in polynomial time, which is important for the large problem instances inthe field. We report empirical results from a first implementation of BPS atthe Technical University of Munich, which allows us to provide importantempirical metrics such as the size of the resulting matching, the average rank,the profile, and the popularity of the assignments. These metrics were centralfor the adoption of BPS. In particular, we compare these metrics to RandomSerial Dictatorship with bundle bids (BRSD). The BRSD mechanism is used tosimulate the wide-spread First-Come First-Served (FCFS) mechanism and it allowsus to compare FCFS (BRSD) and BPS. While theoretically appealing, preferenceelicitation is a major challenge when considering preferences overexponentially many packages. We introduce tools to elicit preferences whichreduce the number of parameters a student needs to a manageable set. Theapproach together with BPS yields a computationally effective tool to solvecourse assignment problems with thousands of students, and possibly provides anapproach for other distributed scheduling tasks in organizations.

Assigning Course Schedules: About Preference Elicitation, Fairness, and Truthfulness