 The Intersection of Machine Learning with Forecasting and Optimisation: Theory and ApplicationsMahdi Abolghasemi ()
Chapter Chapter 12 in Forecasting with Artificial Intelligence, 2023, pp 313-339 from Palgrave Macmillan Abstract: Abstract Forecasting and optimisation are two major fields of operations research that are utilised to deal with uncertainties and to make the best decisions. These methods are widely used in academia and practice and have contributed to each other growth in several ways. These methods can be used together to solve various problems in transportation, scheduling, production planning, and energy where both forecasting and optimisation are needed. However, the nature of the relationship between these two methods and how they can be integrated for better performance have not been explored or understood enough. We advocate the integration of these two methods and explore several problems that require both forecasting and optimisation. I will investigate some of the methodologies that lie at the intersection of machine learning with forecasting and optimisation to address real-world problems. I will provide several research directions and use cases for researchersResearchers and practitioners interested to explore this interesting arena. Date: 2023 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:pal:paiecp:978-3-031-35879-1_12 Ordering information: This item can be ordered from DOI: 10.1007/978-3-031-35879-1_12 Access Statistics for this chapter More chapters in Palgrave Advances in Economics of Innovation and Technology from Palgrave Macmillan |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.