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Alan J. Brown and Harry A. Jackson. Andrew, Allan D. (Allan David), 1966- Massachusetts Institute of Technology. Department of Ocean Engineering 2009-10-01T15:35:15Z 2009-10-01T15:35:15Z 1998 1998 http://hdl.handle.net/1721.1/47723 42461228 Thesis (Nav.E. and S.M.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 1998. Includes bibliographical references (p. 117-118). Ship and submarine design is a very complicated process that requires many trade-offs in design parameters in order to obtain the optimal vehicle effectiveness at the best cost. The number of potential designs is infinite, and the ship designer needs a tool to assist in searching this design space. This thesis uses an evolutionary program to determine the optimal designs of Large Scale Vehicle II, a one-quarter scale submarine model used for propulsor development. A set of designs is randomly generated and represented by binary strings. Each design is treated as an individual in a biological population and evaluated for total ownership cost and two measures of effectiveness. Measures of effectiveness obtained through expert opinion and computer modeling are explored. The designs with high effectiveness and low cost are chosen to produce offspring while the designs with poor effectiveness and high cost are removed from the population. Over many generations, the designs that yield high effectiveness dominate the population. No single design is identified as the optimum. Instead, the information is presented to the decision-maker on a two-dimensional plot that represents the frontier of all non-dominated designs. Each axis represents one of the measures of effectiveness and each level of cost is plotted on a separate curve. This process allows the decision-maker to choose one or several of the non-dominated designs to continue through feasibility and detailed design. by Allan D. Andrew. Nav.E. S.M. 181 p. 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 Ocean Engineering Multi-attribute decision making analysis with evolutionary programming applied to Large Scale Vehicle II Large Scale Vehicle II Thesis application/pdf Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="ecb44acd-4e42-4359-bae1-e03582fabe37"> <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>Multi-attribute decision making analysis with evolutionary programming applied to Large Scale Vehicle II</Title> <Subtitle>Large Scale Vehicle II</Subtitle> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>1998</PublicationDate> <Authors> <Author> <DisplayName>Andrew, Allan D. (Allan David), 1966-</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>Ocean Engineering</Keyword> <Abstract>Ship and submarine design is a very complicated process that requires many trade-offs in design parameters in order to obtain the optimal vehicle effectiveness at the best cost. The number of potential designs is infinite, and the ship designer needs a tool to assist in searching this design space. This thesis uses an evolutionary program to determine the optimal designs of Large Scale Vehicle II, a one-quarter scale submarine model used for propulsor development. A set of designs is randomly generated and represented by binary strings. Each design is treated as an individual in a biological population and evaluated for total ownership cost and two measures of effectiveness. Measures of effectiveness obtained through expert opinion and computer modeling are explored. The designs with high effectiveness and low cost are chosen to produce offspring while the designs with poor effectiveness and high cost are removed from the population. Over many generations, the designs that yield high effectiveness dominate the population. No single design is identified as the optimum. Instead, the information is presented to the decision-maker on a two-dimensional plot that represents the frontier of all non-dominated designs. Each axis represents one of the measures of effectiveness and each level of cost is plotted on a separate curve. This process allows the decision-maker to choose one or several of the non-dominated designs to continue through feasibility and detailed design.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>