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Open Access Journal Article

Machine Learning Approaches to Operations Research Problems

by Daniel Jackson 1,*
1
Daniel Jackson
*
Author to whom correspondence should be addressed.
ORS  2021 3(1):17; https://doi.org/10.69610/j.ors.20210314
Received: 22 January 2021 / Accepted: 26 February 2021 / Published Online: 14 March 2021
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Abstract

The integration of machine learning techniques within operations research has become a pivotal area of research, as these methods offer sophisticated tools for solving complex optimization problems. This paper explores various machine learning approaches applied to operations research problems, highlighting their potential to enhance decision-making processes and improve efficiency. We begin by introducing the foundational principles of operations research and the benefits of incorporating machine learning. Subsequently, we delve into the application of machine learning algorithms in different operations research domains, including network flow optimization, inventory management, and scheduling problems. We discuss the strengths and limitations of each approach, and provide insights into how these methods can be tailored to specific operational contexts. Finally, we offer recommendations for further research in this interdisciplinary field, emphasizing the importance of understanding the underlying assumptions and the potential impact on decision quality.

Keywords: Machine Learning; Operations Research; Optimization; Algorithmic Approaches; Decision-Making.;
Copyright: © 2021 by Jackson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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APA Style
Jackson, D. (2021). Machine Learning Approaches to Operations Research Problems. Operations Research and Statistics, 3(1), 17. doi:10.69610/j.ors.20210314
ACS Style
Jackson, D. Machine Learning Approaches to Operations Research Problems. Operations Research and Statistics, 2021, 3, 17. doi:10.69610/j.ors.20210314
AMA Style
Jackson D. Machine Learning Approaches to Operations Research Problems. Operations Research and Statistics; 2021, 3(1):17. doi:10.69610/j.ors.20210314
Chicago/Turabian Style
Jackson, Daniel 2021. "Machine Learning Approaches to Operations Research Problems" Operations Research and Statistics 3, no.1:17. doi:10.69610/j.ors.20210314

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ACS Style
Jackson, D. Machine Learning Approaches to Operations Research Problems. Operations Research and Statistics, 2021, 3, 17. doi:10.69610/j.ors.20210314
AMA Style
Jackson D. Machine Learning Approaches to Operations Research Problems. Operations Research and Statistics; 2021, 3(1):17. doi:10.69610/j.ors.20210314
Chicago/Turabian Style
Jackson, Daniel 2021. "Machine Learning Approaches to Operations Research Problems" Operations Research and Statistics 3, no.1:17. doi:10.69610/j.ors.20210314
APA style
Jackson, D. (2021). Machine Learning Approaches to Operations Research Problems. Operations Research and Statistics, 3(1), 17. doi:10.69610/j.ors.20210314

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