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Mark Abramson and Hamsa Balakrishnan. Robinson, Eric John, S.M. Massachusetts Institute of Technology Massachusetts Institute of Technology. Operations Research Center. Massachusetts Institute of Technology. Operations Research Center Program in Media Arts and Sciences (Massachusetts Institute of Technology) Sloan School of Management 2014-01-23T17:12:46Z 2014-01-23T17:12:46Z 2013 http://hdl.handle.net/1721.1/84185 867864699 Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2013. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. "June 2013." Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 155-157). collect information. This may include taking pictures of the ground, gathering infrared photos, taking atmospheric pressure measurements, or any conceivable form of data collection. Often these separate organizations have overlapping collection interests or flight plans that are sending sensors into similar regions. However, they tend to be controlled by separate planning systems which operate on asynchronous scheduling cycles. We present a method for coordinating various collection tasks between the planning systems in order to vastly increase the utility that can be gained from these assets. This method focuses on allocation of collection requests to scheduling systems rather than complete centralized planning over the entire system so that the current planning infrastructure can be maintained without changing any aspects of the schedulers. We expand on previous work in this area by inclusion of a learning method to capture information about the uncertainty pertaining to the completion of collection tasks, and subsequently utilize this information in a mathematical programming method for resource allocation. An analysis of results and improvements as compared to current operations is presented at the end. by Eric John Robinson. S.M. 157 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. Coordinated planning of air and space assets : an optimization and learning based approach Thesis application/pdf Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="d6becd39-2c48-4d75-b08d-aa72aae64196"> <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>Coordinated planning of air and space assets : an optimization and learning based approach</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2013</PublicationDate> <Authors> <Author> <DisplayName>Robinson, Eric John, S.M. Massachusetts Institute of Technology</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>collect information. This may include taking pictures of the ground, gathering infrared photos, taking atmospheric pressure measurements, or any conceivable form of data collection. Often these separate organizations have overlapping collection interests or flight plans that are sending sensors into similar regions. However, they tend to be controlled by separate planning systems which operate on asynchronous scheduling cycles. We present a method for coordinating various collection tasks between the planning systems in order to vastly increase the utility that can be gained from these assets. This method focuses on allocation of collection requests to scheduling systems rather than complete centralized planning over the entire system so that the current planning infrastructure can be maintained without changing any aspects of the schedulers. We expand on previous work in this area by inclusion of a learning method to capture information about the uncertainty pertaining to the completion of collection tasks, and subsequently utilize this information in a mathematical programming method for resource allocation. An analysis of results and improvements as compared to current operations is presented at the end.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>