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 20 of 32 for “"optimization under uncertainty"”.
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Optimization under uncertainty in radiation therapy
… for life-threatening illnesses, the presence of uncertainty may compromise the quality of a treatment. In this thesis, we investigate robust approaches to managing uncertainty in radiation therapy treatments for cancer. In the first part of the thesis, we study the effect of breathing motion …
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Approximation algorithms for combinatorial optimization under uncertainty
Combinatorial optimization problems arise in many fields of industry and technology, where they are frequently used in production planning, transportation, and communication network design. Whereas in the context of classical discrete optimization it is usually assumed that the problem inputs are …
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Optimal stopping problems and combinatorial optimization under uncertainty
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms
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Route optimization under uncertainty for Unmanned Underwater Vehicles
… In this thesis, we assume an Unmanned Underwater Vehicle (UUV) is given tasks and must decide which ones to perform (or not perform) in which order. If there is enough time and energy to perform all of the tasks, then the UUV only needs to solve a traveling salesman problem to find the …
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Constraint programming for optimization under uncertainty in inventory control
… information to the search process. We call optimization-oriented global chance-constraints those global chance-constraints performing optimality reasoning. We applied global chance-constraints encapsulating dedicated inference algorithms based on feasibility and/or optimality reasoning to …
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Effective Formulations of Optimization Under Uncertainty for Aerospace Design
Formulations of optimization under uncertainty (OUU) commonly used in aerospace design—those based on treating statistical moments of the quantity of interest (QOI) as separate objectives—can result in stochastically dominated designs. A stochastically dominated design is undesirable, because it is …
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Efficient Optimal and Approximate Algorithms in Optimization Under Uncertainty
… as well as the consequences of these decisions. Optimization under uncertainty has applications in a wide range of settings including hiring (How good is the candidate? Will a better candidate arrive?), disaster relief (Where are people who need help? How long do rescue teams have before they are …
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Dynamic optimization under uncertainty: Applications in engineering, manufacturing, and retail.
L'abstract è presente nell'allegato / the abstract is in the attachment
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Intensity Modulated Proton Therapy Optimization Under Uncertainty: Field Misalignment and Internal Organ Motion
… shapes. However, this flexibility also makes the optimization problems in IMPT harder to solve, e.g., it requires larger memory to store data and longer computational time. Furthermore, proton beams are very sensitive to different uncertainties, such as setup uncertainty, range uncertainty and …
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Dynamic Proton Exchange Membrane Fuel Cell System Synthesis/Design and Operation/Control Optimization under Uncertainty
… highly complex. Typically, the synthesis/design optimization of energy systems is based on a single full load condition at steady state. However, a more comprehensive synthesis/design and operation/control optimization requires taking into account part as well as full load conditions for …
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Optimization Under Uncertainty and Total Predictive Uncertainty for a Tractor-Trailer Base-Drag Reduction Device
… study, and the culminating 3-D aerodynamic shape optimization under uncertainty (OUU) study. To gain confidence in the accuracy and precision of a computational fluid dynamics (CFD) flow solver and its Reynolds-averaged Navier-Stokes (RANS) turbulence models, it is necessary to conduct code …
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Petroleum development optimization under uncertainty : integrating multi-compartment tank models in mixed integer non-linear programs
… of information about the reservoir introduces uncertainty in the analysis done during the screening process of the concept selection and can have a significant impact on the quality of the project. In this work, we present a simple integrated asset model that can be used in conjunction with a …
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Dynamic order allocation for make-to-order manufacturing networks : an industrial case study of optimization under uncertainty/
… and alternate approaches based on mathematical optimization. We develop and analyze three distinct models for these problems which incorporate the firm's data, testing, and feedback, emphasizing realism and usability. The problem is cast as a Dynamic Program with a detailed model of demand …
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Data-driven dynamic optimization with auxiliary covariates
Optimization under uncertainty forms the foundation for many of the fundamental problems the operations research community seeks to solve. In this thesis, we develop and analyze algorithms that incorporate ideas from machine learning to optimize uncertain objectives directly from data. In the first …
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Adaptable optimization : theory and algorithms
Optimization under uncertainty is a central ingredient for analyzing and designing systems with incomplete information. This thesis addresses uncertainty in optimization, in a dynamic framework where information is revealed sequentially, and future decisions are adaptable, i.e., they depend …
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Distributionally robust solution schemes for two-stage optimization and interdiction problems under uncertainty
… often have to make decisions in the presence of uncertainty. One can use optimization models with uncertain parameters to formulate the decision problems. Despite its wide applications in real-world problems, optimization under uncertainty gives rise to computational challenges. This thesis aims …
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Multidelity approaches for design under uncertainty
… of the model outputs. However, performing optimization in this setting can be computationally expensive, requiring many evaluations of the numerical model to compute the statistics of the system metrics, such as the mean and the variance of the system performance. Fortunately, in many …
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Distributionally robust optimization for design under partially observable uncertainty
Deciding how to represent and manage uncertainty is a vital part of designing complex systems. Widely used is a probabilistic approach: assigning a probability distribution to each uncertain parameter. However, this presents the designer with the task of assuming these probability distributions or …
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Temporally Feathered Radiation Therapy under Uncertainty
… therapy planning through novel stochastic optimization models that account for biological heterogeneity and uncertainty in organ-at-risk (OAR) responses. Building on the temporally feathered radiation therapy (TFRT) strategy, we develop a personalized, biologically informed treatment …
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Sparsity and robustness in modern statistical estimation
… of the relationship between robust optimization and a more traditional penalization approach. Further, we show how the threads of optimization under uncertainty and sparse modeling come together by focusing on the trimmed Lasso, a penalization approach to the best subset selection …
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