Operational research and machine learning: An engineering course

Alvaro Talavera; Ana Luna

doi:10.1109/EDUNINE.2019.8875770

Operational research and machine learning: An engineering course

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

In this work, we integrate computational techniques based on machine learning (ML) and computational intelligence (IC) 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 complex problems. The main contribution of this paper is to present a different proposal for OR courses using the synergy between mathematical models employing computer simulations, IC and different hybrid models.

Original language	English
Title of host publication	EDUNINE 2019 - 3rd IEEE World Engineering Education Conference
Subtitle of host publication	Modern Educational Paradigms for Computer and Engineering Career, Proceedings
Editors	Claudio Da Rocha Brito, Melany M. Ciampi
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	978-1-7281-1666-2
DOIs	https://doi.org/10.1109/EDUNINE.2019.8875770
State	Published - 1 Mar 2019
Event	EDUNINE 2019 - 3rd IEEE World Engineering Education Conference: Modern Educational Paradigms for Computer and Engineering Career, Proceedings - Duration: 1 Mar 2019 → …

Conference

Conference	EDUNINE 2019 - 3rd IEEE World Engineering Education Conference: Modern Educational Paradigms for Computer and Engineering Career, Proceedings
Period	1/03/19 → …

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Publisher Copyright:
© 2019 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 4 Quality Education
SDG 9 Industry, Innovation, and Infrastructure
SDG 17 Partnerships for the Goals

Keywords

hybrid models
machine learning
operational research
optimization

Access to Document

10.1109/EDUNINE.2019.8875770

Cite this

Talavera, A., & Luna, A. (2019). Operational research and machine learning: An engineering course. In C. D. R. Brito, & M. M. Ciampi (Eds.), EDUNINE 2019 - 3rd IEEE World Engineering Education Conference: Modern Educational Paradigms for Computer and Engineering Career, Proceedings Article 8875770 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EDUNINE.2019.8875770

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abstract = "In this work, we integrate computational techniques based on machine learning (ML) and computational intelligence (IC) 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 complex problems. The main contribution of this paper is to present a different proposal for OR courses using the synergy between mathematical models employing computer simulations, IC and different hybrid models.",

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author = "Alvaro Talavera and Ana Luna",

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Talavera, A & Luna, A 2019, Operational research and machine learning: An engineering course. in CDR Brito & MM Ciampi (eds), EDUNINE 2019 - 3rd IEEE World Engineering Education Conference: Modern Educational Paradigms for Computer and Engineering Career, Proceedings., 8875770, Institute of Electrical and Electronics Engineers Inc., EDUNINE 2019 - 3rd IEEE World Engineering Education Conference: Modern Educational Paradigms for Computer and Engineering Career, Proceedings, 1/03/19. https://doi.org/10.1109/EDUNINE.2019.8875770

Operational research and machine learning: An engineering course. / Talavera, Alvaro ; Luna, Ana.
EDUNINE 2019 - 3rd IEEE World Engineering Education Conference: Modern Educational Paradigms for Computer and Engineering Career, Proceedings. ed. / Claudio Da Rocha Brito; Melany M. Ciampi. Institute of Electrical and Electronics Engineers Inc., 2019. 8875770.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Operational research and machine learning: An engineering course

Abstract

Conference

Bibliographical note

UN SDGs

Keywords

Access to Document

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