In this work, we integrate computational
techniques based on machine learning (ML) and computational
intelligence (CI) to conventional methodologies used in the
Operational Research (OR) degree course for Engineers. That
synergy between those techniques and methods allows students
to deal with decision-making complex problems. The primary
goals of this research work are to present potential interactions
between the two computational fields and show some examples
of them. This is a contribution to engineering education
research where we present how ML techniques, such as neural
networks, fuzzy logic, and reinforcement learning are
integrated through applications in an OR course, being able to
increase the approach of more complex problems in a simpler
way compared to traditional OR methods. The current paper is
a different proposal for OR courses that uses the symbiosis
between mathematical models employing computer
simulations, CI and different hybrid models.
