[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120484-en":3,"doc-seo-120484-105":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},120484,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Data-driven Dynamic Decision-making - Using Discrete Optimization and Supervised Machine Learning","This Ph.D. thesis advances data-driven optimization for complex combinatorial problems by integrating machine learning with optimization to accelerate online decision-making. In airline operations recovery, it uses supervised machine learning and decision-aware binary classification to prune the decision space, overcoming slow exact methods and weak heuristics while improving runtime and solution quality. In occult hemorrhage diagnosis, it builds a multisensor, vital-sign framework that updates patient risk over time under capacity constraints and optimizes admissions, reducing false positives and missed diagnoses.","Dartmouth College  \nDartmouth Digital Commons  \n\n| Dartmouth College Ph. D Dissertations | Theses and Dissertations |\n| --- | --- |\n| Spring 5-5-2025\u003Cbr>Data-driven Dynamic Decision-making Using Discrete Optimization and Supervised Machine Learning\u003Cbr>Navid Rashedi\u003Cbr>Thayer School of Engineering, [navid.rashedi.th@dartmouth.edu](navid.rashedi.th@dartmouth.edu)\u003Cbr>Follow this and additional works at: [https://digitalcommons.dartmouth.edu/dissertations](https://digitalcommons.dartmouth.edu/dissertations)\u003Cbr> Part of the Industrial Engineering Commons, Operational Research Commons, and the Other Engineering Science and Materials Commons |  |\n\nRecommended Citation  \nRashedi, Navid, \"Data-driven Dynamic Decision-making Using Discrete Optimization and Supervised Machine Learning\" (2025) . Dartmouth College Ph. D Dissertations. 350.  \n[https://digitalcommons.dartmouth.edu/dissertations/350](https://digitalcommons.dartmouth.edu/dissertations/350)  \nThis Thesis (Ph. D.) is brought to you for free and open access by the Theses and Dissertations at Dartmouth Digital Commons. It has been accepted for inclusion in Dartmouth College Ph. D Dissertations by an authorized administrator of Dartmouth Digital Commons. For more information, please contact [dartmouthdigitalcommons@groups.dartmouth.edu](dartmouthdigitalcommons@groups.dartmouth.edu).  \nData-driven Dynamic Decision-making Using Discrete Optimization and Supervised  \nMachine Learning  \nA Thesis  \nSubmitted to the Faculty in partial fulfillment of the requirements for the degree of  \nDoctor of Philosophy  \nin  \nEngineering Sciences  \nby Navid Rashedi  \nThayer School of Engineering  \nGuarini School of Graduate and Advanced Studies  \nDartmouth College  \nHanover, New Hampshire  \nMay 2025  \nExamining Committee:  \nChairman   Vikrant Vaze  \nMember   Jonathan T. Elliott  \nMember   Eugene Santos  \nMember   Alexandre Jacquillat  \nF. Jon Kull, Ph.D.  \nDean of Guarini School of Graduate and Advanced Studies  \nAbstract  \nIn recent years, the operations research community has developed data-driven optimization techniques to solve complex combinatorial problems with the aid of machine learning. This thesis contributes to these efforts by combining machine learning with optimization to expedite online decision-making, with applications in transportation and healthcare.  \nIn the domain of airline operations recovery, the focus is on the aircraft recovery process—repairing disrupted schedules by minimizing overall disruption costs. Traditional exact methods are too time-consuming, while heuristic approaches often yield poor solution quality and lack generalizability across varying formulations. To address these challenges, this research employs supervised machine learning to identify near-optimal solution components by leveraging historical data. By integrating binary classification methods into a decision-aware framework, our approach prunes the decision space effectively, yielding high-quality solutions in significantly shorter runtimes than both exact and heuristic methods.  \nIn the healthcare domain, for the diagnosis of occult hemorrhage, we develop a datadriven framework that takes advantage of multisensor data and vital signs to detect internal bleeding early, particularly under capacity constraints. We conduct extensive experiments with animal and human data to engineer high-fidelity features and maximize predictive accuracy. Building on these predictions, we propose a decision-aware machine learning approach that dynamically updates patient risk scores over time and optimizes admissions to resource-intensive care units. Our method balances the trade-off between acting early (with less accurate information) versus waiting for more precise data, thus reducing both false positives and missed diagnoses. Through experiments based on data sets from our preclinical study, we show that our dynamic optimization framework surpasses traditional risk-based heuristics, leading to significantly improved pat","cbCaicJxYXEnfyPg","https://ap.wps.com/l/cbCaicJxYXEnfyPg","pdf",2746641,1,167,"English","en",105,"# Abstract\n## Airline operations recovery\n## Healthcare diagnosis of occult hemorrhage\n## Experimental results and outcomes\n# Acknowledgements","[{\"question\":\"How does the thesis improve online decision-making for combinatorial problems?\",\"answer\":\"It combines supervised machine learning with decision-aware optimization to identify near-optimal components from historical data, pruning the decision space and accelerating online decisions.\"},{\"question\":\"What problem is addressed in the airline operations recovery application?\",\"answer\":\"It focuses on aircraft recovery by repairing disrupted schedules through minimizing overall disruption costs, aiming to outperform slow exact methods and less generalizable heuristics.\"},{\"question\":\"How does the healthcare part handle occult hemorrhage diagnosis under capacity constraints?\",\"answer\":\"It uses multisensor data and vital signs to detect internal bleeding early, then dynamically updates patient risk scores and optimizes admissions to resource-intensive care units while balancing early action versus waiting for more precise data.\"}]","Data-driven Dynamic Decision-making - 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