Finding minimum-cost circulations by canceling negative cycles
Journal of the ACM (JACM)
Finding a feasible course schedule using Tabu search
Discrete Applied Mathematics - Special issue: Timetabling and chromatic scheduling
Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
An efficient implementation of a scaling minimum-cost flow algorithm
Journal of Algorithms
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A Survey of Automated Timetabling
Artificial Intelligence Review
Erratum: The Stable Allocation (or Ordinal Transportation) Problem
Mathematics of Operations Research
Recent Developments in Practical Course Timetabling
PATAT '97 Selected papers from the Second International Conference on Practice and Theory of Automated Timetabling II
Used car salesman problem: A differential auction--barter market
Annals of Mathematics and Artificial Intelligence
Resource bartering in data grids
Scientific Programming
A multiobjective faculty-course-time slot assignment problem with preferences
Mathematical and Computer Modelling: An International Journal
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Even though course timetabling and student scheduling problems have been studied extensively, not much has been done for the optimization of student add/drop requests after the initial registration period. Add/drop registrations are usually processed with a first come first served policy. This, however, can introduce inefficiencies and dead-locks resulting in add/drop requests that are not satisfied even though they can, in fact, be satisfied. We model the course add/drop process as a direct bartering problem in which add/drop requests appear as bids. We formulate the resulting problem as an integer linear program. We show that our problem can be solved polynomially as a minimum cost flow network problem. In our model, we also introduce a two-level weighting system that enables students to express priorities among their requests. We demonstrate improvement in the satisfaction of students over the currently used model and also the fast performance of our algorithms on various test cases based on real-life registration data of our university.