Bipartite Modeling for Solving the University Timetable Problem

This work proposes the creation of a bipartite mathematical model for optimizing Curriculum based University Course Timetabling at the Institute of Computing (IC) of the Federal University of Rio de Janeiro (UFRJ), automating the allocation of courses and classrooms to optimize resource usage and reduce common errors in the still widely used manual process. The model separates classroom allocation from the assignment of professors to courses, treating them as two distinct yet interconnected problems, making the process more flexible and adaptable to other scenarios. For the development of both models, best practices from the industry and software engineering were considered to ensure that the solution is robust, scalable, and adaptable in the future. The research was conducted through a case study, which involved requirements mapping, mathematical modeling, and result validation using combinatorial optimization techniques and mathematical programming. The implementation was carried out using the Gurobi solver in Python. The flexibility of the proposed model allows for future adaptations to changes in the academic guidelines of the IC and other programs within the Center for Mathematical and Natural Sciences (CCMN) at UFRJ. Furthermore, the simulations performed showed promising results for real-world application, demonstrating the potential to optimize resources such as time and workload capacity. Thus, the proposed model enables better utilization of institutional resources and provides a more efficient scheduling solution.