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The intersection of machine learning with forecasting and optimisation: theory and applications

arXiv 2211.13583 ↗First posted 24 Nov 2022cs.LG math.OC stat.MEcited by 0

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Abstract (arXiv)

Forecasting and optimisation are two major fields of operations research that are widely used in practice. These methods have contributed to each other growth in several ways. However, the nature of the relationship between these two fields and integrating them have not been explored or understood enough. We advocate the integration of these two fields and explore several problems that require both forecasting and optimisation to deal with the uncertainties. We further investigate some of the methodologies that lie at the intersection of machine learning with prediction and optimisation to address real-world problems. Finally, we provide several research directions for those interested to work in this domain.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 39 references that are in the map. First posted on arXiv 24 Nov 2022.

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References · 20

  1. 2015Pointer Networkscited by 895
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8 more
  1. 2020Decision Trees for Decision-Making under the Predict-then-Optimize Frameworkcited by 53
  2. 2020Differentially Private and Fair Deep Learning: A Lagrangian Dual Approachcited by 53
  3. 2020Contrastive Losses and Solution Caching for Predict-and-Optimizecited by 52
  4. 2020Teaching the Old Dog New Tricks: Supervised Learning with Constraintscited by 16
  5. 2020High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flowcited by 13
  6. 2018Predicting Tactical Solutions to Operational Planning Problems under Imperfect Informationcited by 11
  7. 2021State-of-the-art predictive and prescriptive analytics for IEEE CIS 3rd Technical Challengecited by 8
  8. 2021How to effectively use machine learning models to predict the solutions for optimization problems: lessons from loss functioncited by 5

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