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Dimitris Bertsimas and Julie Shah. Frost, Emily Anne Massachusetts Institute of Technology. Operations Research Center. Massachusetts Institute of Technology. Operations Research Center Sloan School of Management 2014-02-10T16:54:51Z 2014-02-10T16:54:51Z 2013 http://hdl.handle.net/1721.1/84854 868235613 Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2013. Cataloged from PDF version of thesis. Includes bibliographical references (pages 59-60). In this thesis, I design and implement a novel method of schedule and path selection between predetermined waypoints for unmanned underwater vehicles under uncertainty. The problem is first formulated as a mixed-integer optimization model and subsequently uncertainty is addressed using a robust optimization approach. Solutions were tested through simulation and computational results are presented which indicate that the robust approach handles larger problems than could previously be solved in a reasonable running time while preserving a high level of robustness. This thesis demonstrates that the robust methods presented can solve realistic-sized problems in reasonable runtimes - a median of ten minutes and a mean of thirty minutes for 32 tasks - and that the methods perform well both in terms of expected reward and robustness to disturbances in the environment. The latter two results are obtained by simulating solutions given by the deterministic method, a naive robust method, and finally the two restricted affine robust policies. The two restricted affine policies consistently show an expected reward of nearly 100%, while the deterministic and naive robust methods achieve approximately 50% of maximum reward possible. by Emily Anne Frost. S.M. 60 pages eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Operations Research Center. Robust planning for unmanned underwater vehicles Robust planning for UUVs Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="31dc22df-131f-4afd-882a-eafef728219d"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Robust planning for unmanned underwater vehicles</Title> <Subtitle>Robust planning for UUVs</Subtitle> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2013</PublicationDate> <Authors> <Author> <DisplayName>Frost, Emily Anne</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Operations Research Center.</Keyword> <Abstract>In this thesis, I design and implement a novel method of schedule and path selection between predetermined waypoints for unmanned underwater vehicles under uncertainty. The problem is first formulated as a mixed-integer optimization model and subsequently uncertainty is addressed using a robust optimization approach. Solutions were tested through simulation and computational results are presented which indicate that the robust approach handles larger problems than could previously be solved in a reasonable running time while preserving a high level of robustness. This thesis demonstrates that the robust methods presented can solve realistic-sized problems in reasonable runtimes - a median of ten minutes and a mean of thirty minutes for 32 tasks - and that the methods perform well both in terms of expected reward and robustness to disturbances in the environment. The latter two results are obtained by simulating solutions given by the deterministic method, a naive robust method, and finally the two restricted affine robust policies. The two restricted affine policies consistently show an expected reward of nearly 100%, while the deterministic and naive robust methods achieve approximately 50% of maximum reward possible.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>