Optimization under Uncertainty: Machine Learning Approach
عنوان مقاله: Optimization under Uncertainty: Machine Learning Approach
شناسه ملی مقاله: JR_IJIMES-3-2_003
منتشر شده در در سال 1402
شناسه ملی مقاله: JR_IJIMES-3-2_003
منتشر شده در در سال 1402
مشخصات نویسندگان مقاله:
Reza Mohammadi * - Department of Industrial Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran
Hasan Farsijani - Associated Professor of Industrial Management, Shahid Beheshti University, Tehran, Iran
خلاصه مقاله:
Reza Mohammadi * - Department of Industrial Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran
Hasan Farsijani - Associated Professor of Industrial Management, Shahid Beheshti University, Tehran, Iran
Data is the new oil. From the beginning of the ۲۱st century, data is similar to what oil was in the ۱۸th century, an immensely untapped valuable asset. This paper reviews recent advances in the field of optimization under uncertainty via a modern data lens and highlights key research challenges and the promise of data-driven optimization that organically integrates machine learning and mathematical programming for decision-making under uncertainty. A brief review of classical mathematical programming techniques for hedging against uncertainty is first presented, along with their wide spectrum of applications in Process Systems Engineering. we provide an introduction to the topic of uncertainty in machine learning as well as an overview of attempts so far at handling uncertainty in general and formalizing this distinction in particular. In line with the statistical tradition, uncertainty has long been perceived as almost synonymous with standard probability and probabilistic predictions. Yet, due to the steadily increasing relevance of machine learning for practical applications and related issues such as safety requirements, new problems, and challenges have recently been identified by machine learning scholars, and these problems may call for new methodological developments.Data is the new oil. From the beginning of the ۲۱st century, data is similar to what oil was in the ۱۸th century, an immensely untapped valuable asset. This paper reviews recent advances in the field of optimization under uncertainty via a modern data lens and highlights key research challenges and the promise of data-driven optimization that organically integrates machine learning and mathematical programming for decision-making under uncertainty. A brief review of classical mathematical programming techniques for hedging against uncertainty is first presented, along with their wide spectrum of applications in Process Systems Engineering. we provide an introduction to the topic of uncertainty in machine learning as well as an overview of attempts so far at handling uncertainty in general and formalizing this distinction in particular. In line with the statistical tradition, uncertainty has long been perceived as almost synonymous with standard probability and probabilistic predictions. Yet, due to the steadily increasing relevance of machine learning for practical applications and related issues such as safety requirements, new problems, and challenges have recently been identified by machine learning scholars, and these problems may call for new methodological developments.
کلمات کلیدی: Optimization, Supply Chain, Uncertainty, Machine Learning
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/1912115/