[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116885-en":3,"doc-seo-116885-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},116885,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Optimization under Uncertainty - Machine Learning Approach","Data functions as a high-value yet underused asset, driving renewed interest in optimization under uncertainty through a modern data perspective. The study surveys recent progress by connecting data-driven optimization with machine learning and mathematical programming for decision-making under uncertainty. It reviews classical mathematical programming methods for hedging against uncertainty and discusses their broad application in process systems engineering. It also examines how uncertainty in machine learning is formulated and handled, contrasting traditional probabilistic views with emerging challenges tied to practical machine learning relevance.","ISSN: 2783-2678  \nInternational journal of Innovation in Management Economics and Social Sciences  \n[Journal homepage: ](Journal homepage: www.ijimes.ir)[www.ijimes.ir](Journal homepage: www.ijimes.ir)  \nInt. J. Inn. Man. Eco. Soc. Sci. Vol. 3, No, 2, 23-32.  \nOptimization under Uncertainty: Machine Learning Approach  \nReza Mohammadi 1,*, Hasan Farsijani2  \n1 Department of Industrial Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran  \n2 Associated Professor of Industrial Management, Shahid Beheshti University, Tehran, Iran  \n\n| ARTICLE INFO | ABSTRACT |\n| --- | --- |\n| Received: 18 February 2023\u003Cbr>Reviewed:10 March 2023\u003Cbr>Revised: 1 April 2023\u003Cbr>Accept:29 April 2023 | Data is the new oil. From the beginning of the 21st century, data is similar to what oil was in the 18th century, an immensely untapped valuable asset. This paper reviews recent advances in the field of optimization under uncertainty via a modern data lens, highlights key research challenges and promise of data-driven optimization that organically integrates machine learning and mathematical programming for decision-making under uncertainty.\u003Cbr>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.\u003Cbr>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.\u003Cbr>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. |\n| Keywords: Optimization, Supply Chain, Uncertainty, Machine Learning |  |\n\n* Corresponding Author: [rmohammadi@bakhtargroup.com](rmohammadi@bakhtargroup.com)  \n1. Introduction  \nWhile methods for optimization under uncertainty have been studied intensely over the past decades, the explicit consideration of the interplay between uncertainty and time has gained increasing attention rather recently. Problems requiring a sequence of decisions in reaction to uncertainty realizations are of crucial relevance in real-world applications, e.g., supply chain planning, scheduling, or finance. Several methods emphasizing varying aspects of these problems have been developed, mainly triggered by a particular application. Although these methods all intend to solve a similar underlying problem, they differ strongly with respect to the uncertainty representation, the prescriptive solution information they provide and the means of performance evaluation. Over the last six decades, several pioneers of the industry have worked to steer us in the right direction [ 1] . Uncertainty and fuzziness are popular phenomena in many application areas such as medicine (medical diagnosis is often not crisp but there exist various degrees of illness e.g. for psychical diseases such as phobia), image processing (areas at object borders or at overlapping regions can seldom uniquely be classified), linguistics (terms such as ‗high‘ or ‗small‘ are context dependent), etc. Therefore, uncertainty almost automatically occurs in any application of machine learning [2] . An important aspect of these systems is the complete and valid quantification of model uncertainty [  3] .  \nFrom early times people have realized that managing a situation that included many alternatives, is nothing less and nothing more than determining the solution with the more positive and less negative consequences. The procedure to determine the ―ideal‖ solution is called optimization. The oldest optimization t","cbCaibwcL83UmNlv","https://ap.wps.com/l/cbCaibwcL83UmNlv","pdf",520117,1,10,"English","en",105,"# Article Info\n# Abstract\n# Introduction\n## Uncertainty and fuzziness in applications\n## Optimization fundamentals and modeling constraints\n## Aleatoric vs epistemic uncertainty and estimation tools\n# Uncertainty and Optimization under Uncertainty","[{\"question\":\"What is the central theme of the paper?\",\"answer\":\"The paper reviews optimization under uncertainty by linking a data-driven viewpoint with machine learning and mathematical programming to support decision-making under uncertainty.\"},{\"question\":\"How does the paper introduce uncertainty in machine learning?\",\"answer\":\"It presents uncertainty as a key aspect of machine learning and explains how uncertainty is quantified and distinguished in general, and then formalized more specifically for the learning setting.\"},{\"question\":\"What are aleatoric and epistemic uncertainty?\",\"answer\":\"Aleatoric uncertainty is inherent in the process, while epistemic uncertainty comes from inadequate knowledge of the model best suited to explain the data.\"}]","Optimization under Uncertainty - 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