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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.
To retrieve all theses and dissertations associated with a specific department from the library catalogue, choose 'Advanced' and keyword search "university of alberta dept of english" OR "university of alberta department of english" (for example). Past graduates who wish to have their thesis or dissertation added to this collection can contact us at erahelp@ualberta.ca.
Items in this Collection
- 2Robust optimization
- 1Affine decision rule
- 1Chance constraint
- 1Data reconciliation
- 1Gross error detection
- 1Optimal design and operation
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Fall 2017
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
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Fall 2019
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 significantcant 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