This article presents an optimization model for designing a supervision visit schedule for sales teams. The model is built on a set of constraints that consider the availability of both the sales team and supervisor, considering the days available for conducting supervisions. Various combinatorial modeling approaches are used to satisfy these constraints. The solution provides a plan for sales team outings that is compared against the performance of empirical schedules implemented between 2015 and 2024, allowing for an assessment of the efficiency of the proposed solutions. The model's constraints are adapted to the specific needs of the sales team and the dynamics observed in the daily operational environment. Results indicate that the proposed optimization models improve operational efficiency and significantly contribute to the early detection of performance issues within the sales team, facilitating timely and strategic corrective interventions to maximize the overall performance of the commercial organization.

Course timetabling represents a common and often daunting problem in universities. Manual solutions are labor-intensive and, if larger enough, even impossible to achieve. Our approach is focused on professor assignments to courses, specifically addressing their preferences for each time slot. We carry out a Post-Enrolment guided formulation but clustering the students in groups that are enrolled to courses. Our motivation stems from automating the timetabling process regarding the context of size-reduced universities in Uruguay, with potential applicability to other South American institutes. Thus, room assignment and capacity restrictions are excluded in our present scope. Our formulation was tested for the 1st_grade courses at the Facultad de Ingeniería of the Universidad de Montevideo (UM) –5 groups of approximately 30 students–, yielding reliable solutions using the Gurobi 10 solver. Future work involves more comprehensive cases and practical implementation for real-world timetabling decisions at UM. For this reason, we aim to define rules mapping special or abstract requirements to our numerical scale priorities parameter.