RT info:eu-repo/semantics/article
T1 Aprendizaje Automático: Una Contribución a la Investigación Operativa
A1 Talavera, Alvaro
A1 Luna, Ana
K1 operational research
K1 machine learning
K1 optimization
K1 hybrid models
AB In this work, we integrate computationaltechniques based on machine learning (ML) and computationalintelligence (CI) to conventional methodologies used in theOperational Research (OR) degree course for Engineers. Thatsynergy between those techniques and methods allows studentsto deal with decision-making complex problems. The primarygoals of this research work are to present potential interactionsbetween the two computational fields and show some examplesof them. This is a contribution to engineering educationresearch where we present how ML techniques, such as neuralnetworks, fuzzy logic, and reinforcement learning areintegrated through applications in an OR course, being able toincrease the approach of more complex problems in a simplerway compared to traditional OR methods. The current paper isa different proposal for OR courses that uses the symbiosisbetween mathematical models employing computersimulations, CI and different hybrid models.
YR 2020
FD 2020
LK http://riull.ull.es/xmlui/handle/915/40204
UL http://riull.ull.es/xmlui/handle/915/40204
LA es
DS Repositorio institucional de la Universidad de La Laguna
RD 23-dic-2024