MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Graduate Theses
  • View Item
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Graduate Theses
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Dealing with uncertainty : a comparison of robust optimization and partially observable Markov decision processes

Author(s)
Horgan, Casey Vi
Thumbnail
DownloadFull printable version (8.919Mb)
Alternative title
Comparison of robust optimization and partially observable Markov decision processes
Other Contributors
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics.
Advisor
Michael J. Ricard and Leslie P. Kaelbling.
Terms of use
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582
Metadata
Show full item record
Abstract
Uncertainty is often present in real-life problems. Deciding how to deal with this uncertainty can be difficult. The proper formulation of a problem can be the larger part of the work required to solve it. This thesis is intended to be used by a decision maker to determine how best to formulate a problem. Robust optimization and partially observable Markov decision processes (POMDPs) are two methods of dealing with uncertainty in real life problems. Robust optimization is used primarily in operations research, while engineers will be more familiar with POMDPs. For a decision maker who is unfamiliar with one or both of these methods, this thesis will provide insight into a different way of problem solving in the presence of uncertainty. The formulation of each method is explained in detail, and the theory of common solution methods is presented. In addition, several examples are given for each method. While a decision maker may try to solve an entire problem using one method, sometimes there are natural partitions to a problem that encourage using multiple solution methods. In this thesis, one such problem is presented, a military planing problem consisting of two parts. The first part is best solved with POMDPs and the second with robust optimization. The reasoning behind this partition is explained and the formulation of each part is presented. Finally, a discussion of the problem types suitable for each method, including multiple applications, is provided.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2017.
 
Cataloged from PDF version of thesis.
 
Includes bibliographical references (pages 131-132).
 
Date issued
2017
URI
http://hdl.handle.net/1721.1/112410
Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Publisher
Massachusetts Institute of Technology
Keywords
Aeronautics and Astronautics.

Collections
  • Graduate Theses

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.