Student Course Allocation with Constraints

  • Akshay UttureEmail author
  • Vedant Somani
  • Prem Krishnaa
  • Meghana Nasre
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11544)


Real-world matching scenarios, like the matching of students to courses in a university setting, involve complex downward-feasible constraints like credit limits, time-slot constraints for courses, basket constraints (say, at most one humanities elective for a student), in addition to the preferences of students over courses and vice versa, and class capacities. We model this problem as a many-to-many bipartite matching problem where both students and courses specify preferences over each other and students have a set of downward-feasible constraints. We propose an Iterative Algorithm Framework that uses a many-to-one matching algorithm and outputs a many-to-many matching that satisfies all the constraints. We prove that the output of such an algorithm is Pareto-optimal from the student-side if the many-to-one algorithm used is Pareto-optimal from the student side. For a given matching, we propose a new metric called the Mean Effective Average Rank (MEAR), which quantifies the goodness of allotment from the side of the students or the courses. We empirically evaluate two many-to-one matching algorithms with synthetic data modeled on real-world instances and present the evaluation of these two algorithms on different metrics including MEAR scores, matching size and number of unstable pairs.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Akshay Utture
    • 2
    Email author
  • Vedant Somani
    • 1
  • Prem Krishnaa
    • 1
  • Meghana Nasre
    • 1
  1. 1.Indian Institute of Technology MadrasChennaiIndia
  2. 2.University of CaliforniaLos AngelesUSA

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