Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 11 of 11 for “"Prescriptive Analytics"”.
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Predictive and Prescriptive Analytics in Operations Management
… the recent developments and democratization of analytics highlight the critical importance of prediction and prescription in harnessing the power of data to create value through optimal, data-driven decision making. This thesis proposes novel Machine Learning (ML) and optimization methods in (i) …
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Predictive and Prescriptive Analytics for Airport Slot Allocation
Slot allocation is the primary form of strategic demand management practiced at airports globally to address congestion and reduce delay. To perform slot allocation, airport schedulers must account for detailed requests from hundreds of airlines for thousands of flights over a six-month season …
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Prescriptive analytics in operations problems : a tree ensemble approach
… concern addressing challenges in the field of prescriptive optimization, and how machine learning techniques can be incorporated into solving data-driven operational optimization problems. In chapter 2, we provide a data-driven study of the secondary ticket market. In particular we are …
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New algorithms in machine learning with applications in personalized medicine
… machine learning, and develop new methods for prescriptive analytics. I show examples of how common machine learning tasks, such as missing data imputation in Chapter 2 and classication in Chapter 3, can benet from the added edge of rigorous optimization formulations and solution techniques. In …
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Natural Language Interface for Prescriptive AI Solutions in Enterprise
Despite advancements in causal inference and prescriptive AI, its adoption in enterprise settings remains hindered primarily due to its complexity and lack of interpretability. This work at the MIT-IBM Watson AI Lab focuses on extending upon the proof-of-concept agent, PrecAIse, by designing a …
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Machine Learning for Downstream Oil & Gas Refineries: Applications for Solvent Deasphalting
… the proposed approach, combining predictive and prescriptive analytics, can result in significant profitability gains estimated at $730,000 annually. The results of this model can be utilized for more accurate plant monitoring within oil & gas downstream refineries, as well as improved decision …
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Large-scale Optimization for Robust Multi-Class Prediction and Resource Allocation
… of robust multi-class prediction and second for prescriptive analytics for medical resource allocation. In the first part, we make progress on training robust multi-class classifiers using error-correcting output codes (ECOC). We propose linear and non-linear integer programming (IP) formulations …
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From data to decisions through new interfaces between optimization and statistics
… and revealing applications that advance data analytics from a tool of understanding to a decision-making engine. In part I, we focus on the interface between predictive and prescriptive analytics. In the first half, we combine ideas from machine learning and operations research to prescribe …
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Practical Considerations When Using Predictive Models Within Optimization Problems: Issues, Options, and Applications
… when incorporating predictive models within prescriptive optimization models. We provide a scholarly research stream and suggest industry best practices by identifying novel modeling considerations that can reduce decision support risk. Specifically, we provide a practical two-stage …
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Artificial Intelligence for System Medicine: Methods and Applications
… frequency. Finally, we apply predictive and prescriptive analytic methods to improve general medical outcomes in Part 3 and Part 4, respectively. These studies range from oncology, trauma, cardiovascular, and logistics planning. In Part 3, we aim to develop models that can most accurately …
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An Analytics Approach To Reducing Hospital Readmission
… this dissertation we investigated predictive and prescriptive analytics approaches for discharge planning and hospital readmission problem. Motivated by the gaps in research, we first develop a new readmission metric based on administrative data that can identify potentially avoidable readmissions …