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Theses and Dissertations

This collection contains theses and dissertations of graduate students of the University of Alberta. The collection contains a very large number of theses electronically available that were granted from 1947 to 2009, 90% of theses granted from 2009-2014, and 100% of theses granted from April 2014 to the present (as long as the theses are not under temporary embargo by agreement with the Faculty of Graduate and Postdoctoral Studies). IMPORTANT NOTE: To conduct a comprehensive search of all UofA theses granted and in University of Alberta Libraries collections, search the library catalogue at www.library.ualberta.ca - you may search by Author, Title, Keyword, or search by Department.
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Results for "Probability Distributions on a Circle"

Subject: Robust optimization

  • Process Data Rectification and Process Optimization with Uncertainty

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    Fall 2017

    Yuan, Yuan

    reconcile the data. Among various approaches for optimization with uncertainty, chance constraint problem is a natural way to quantify the reliability of the solutions by setting a restriction on the level of the probability that the constraints are satisfied. In the case that multiple constraints should be

    satisfied simultaneously, joint chance constraint is appropriate to model the uncertainties. However, joint chance constraint problem is generally intractable and a variety of methods are available to approximate it into tractable forms. Robust optimization with the distribution-free property is an approach

    models. This thesis develops a novel robust optimization framework to consider the uncertain nonlinear optimization problems. The thesis provides practical applications as well. An economic optimization problem is investigated for steam generation and water distribution for SAGD (steam-assisted-gravity

  • Nonlinear Robust Optimal Design and Operation of Effluent Treatment Systems

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    Fall 2019

    Kammammettu, Sanjula

    uncertainty realized and presents a conservative solution to the problem that would be valid for any realization of uncertainty it was solved for. In contrast, stochastic optimization deals with uncertainty in an optimization problem by assuming that the probability distribution of the uncertainty is known

    presents an additional layer of complexity owing to the presence of uncertainty in the operation of the system. This uncertainty may come from a variety of sources, such as effluent flow rate, contaminant concentration, and treatment unit removal efficiency. Therefore, the need to focus on developing a

    The optimal design and operation of effluent treatment system networks poses a significant cant challenge in the present time, with the imposition of stricter environmental regulations and an increased demand for resources exacerbated by a diminishing resource pool. In practice, this problem

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