EURO 2024 Copenhagen
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812. Integration of Operations Research (OR) and Machine Learning (ML) – A Literature Review

Invited abstract in session WA-3: Data Science and Optimization, stream Data Science Meets Optimization.

Wednesday, 8:30-10:00
Room: 1005 (building: 202)

Authors (first author is the speaker)

1. Francis Miranda
Business School Lausanne

Abstract

There is an increasing trend of combining Operations Research (OR) with Machine Learning (ML) techniques. Both disciplines are already very beneficial when used independently. Combining both techniques will even enhance the benefits. The author starts with a brief historical overview on Operations Research (OR) and Machine Learning (ML), tracing the important milestones in each field. Then the author talks about the different categories and techniques used in Operations Research and Machine Learning, highlighting the diverse methodologies such as linear programming, mixed integer programming and network optimization in OR, together with supervised, unsupervised learning and reinforcement learning in ML.
Next, the author talks about the practice of Operations Research and Machine Learning, including the educational background and job roles or functions of practitioners in the field, the software tools and the most common techniques or algorithms used. The author also discusses the different applications in various industries. The author will highlight the benefits and limitations of Operations Research (OR) and Machine Learning (ML) when used independently. Then, the author explores the ways both techniques are integrated based on the literature such using ML then OR, using ML in OR, and using OR in ML. Finally, the author will mention some case studies from the literature how companies leveraged on both techniques to enhance their operational decision-making process.

Keywords

Status: accepted

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