Optimization under Uncertainty: Machine Learning Approach
- سال انتشار: 1402
- محل انتشار: ژورنال بین المللی نوآوری در مدیریت، اقتصاد و علوم اجتماعی، دوره: 3، شماره: 2
- کد COI اختصاصی: JR_IJIMES-3-2_003
- زبان مقاله: انگلیسی
- تعداد مشاهده: 46
نویسندگان
Department of Industrial Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran
Associated Professor of Industrial Management, Shahid Beheshti University, Tehran, Iran
چکیده
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.کلیدواژه ها
Optimization, Supply Chain, Uncertainty, Machine Learningاطلاعات بیشتر در مورد COI
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