Title:

Machine Learning: A Contribution to Operational Research [Descargar]

Autores/as:

Talavera, Alvaro and Luna, Ana

Términos Índice:

Machine learning;Decision making;Operations research;Fuzzy logic;Mathematical model;Optimization;Operational research;machine learning;optimization;hybrid models

Resumen:

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.

DOI:

10.1109/RITA.2020.2987700

Cómo citar:

Talavera, Alvaro and Luna, Ana, "Machine Learning: A Contribution to Operational Research" in IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, pp. 70-75, May. 2020. doi: 10.1109/RITA.2020.2987700