OPTIMIZATION MODEL FOR THE GENERATION OF TIMETABLE IN AN UNDERGRADUATE COURSE

The assembly of the timetable by the students themselves in higher education is a recurrent and meticulous task, conditioned by factors such as schedule shocks, subject limits and different levels of difficulty of the subjects. In the Production Engineering course at CEFET/RJ, Itaguaí Campus, this process is carried out manually, which can result in unbalanced grades and delays in curricular integration. This work presents a model of Mixed Integer Linear Programming (PLIM) to support the personalized selection of subjects by students, considering academic criteria such as period, prerequisites, curricular dependencies and perceived difficulty. The approach integrates three stages: survey of the perceived difficulty via a Likert questionnaire applied to 67 students and graduates; automatic pre-processing of eligible courses based on the student's academic record; and optimization via PLIM, selecting the highest priority set under constraints of overlapping schedule, enrollment limits, and maximum difficulty per semester. The model was implemented in Python with the Gurobi solver and tested in 50 real instances, producing feasible and balanced grids in tenths of a millisecond, with zero optimality gap in all scenarios. The comparison with the open-source PuLP/CBC solver confirmed the same solution quality, with Gurobi about 15 times faster. The results indicate the feasibility of interactive use by students and support for academic coordinators, such as in the monitoring of students in critical situations