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Jung-Hoon Chun and Stephen Graves. Páez, Daýan Massachusetts Institute of Technology. Dept. of Mechanical Engineering. Massachusetts Institute of Technology. Department of Mechanical Engineering 2010-09-02T17:23:38Z 2010-09-02T17:23:38Z 2010 2010 http://hdl.handle.net/1721.1/58288 650085443 Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2010. Cataloged from PDF version of thesis. Includes bibliographical references (p. 93). A computer model to optimize global expansion of the production of solar panels is presented. The model is modular, extensible, and fast compared to existing specialized optimization software which use integer linear programming. The model inputs are (1) a tree of the assembly, or bill of materials (BOM), (2) a set of candidate locations where to build the product and any or all of its subcomponents, and (3) other cost drivers. As a tool for expansion, the model accounts for an already-existing manufacturing location that can expand production of one or more of the components. The number of factories to build per location is discrete. A full-combinatorial exploration of the parameter space is used to optimize recursively at every level of the BOM. The program output delineates where each component should be produced, and where and how much of it should be shipped, along with the associated costs. A second program operates in reverse: given a sourcing strategy, it outputs the net cost. In tandem, the two halves of the expansion model are used to explore parameter sensitivity and solution robustness of various hypothetical case studies. These tests reveal critical time horizons for expansion and the relative importance of material costs in driving the optimal sourcing scenario. Finally, a discussion on how to extend the programs is provided. The programs successfully account for the different nature of each cost driver; optimize according to the given constraints; and provide a fast, scriptable interface for parameter testing. by Daýan Páez. S.M. 142 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 Mechanical Engineering. Discrete, recursive supply chain model for solar panel manufacturing Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="3eb2d525-1744-4a5c-a3d3-51352254a0f6"> <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>Discrete, recursive supply chain model for solar panel manufacturing</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2010</PublicationDate> <Authors> <Author> <DisplayName>Páez, Daýan</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>Mechanical Engineering.</Keyword> <Abstract>A computer model to optimize global expansion of the production of solar panels is presented. The model is modular, extensible, and fast compared to existing specialized optimization software which use integer linear programming. The model inputs are (1) a tree of the assembly, or bill of materials (BOM), (2) a set of candidate locations where to build the product and any or all of its subcomponents, and (3) other cost drivers. As a tool for expansion, the model accounts for an already-existing manufacturing location that can expand production of one or more of the components. The number of factories to build per location is discrete. A full-combinatorial exploration of the parameter space is used to optimize recursively at every level of the BOM. The program output delineates where each component should be produced, and where and how much of it should be shipped, along with the associated costs. A second program operates in reverse: given a sourcing strategy, it outputs the net cost. In tandem, the two halves of the expansion model are used to explore parameter sensitivity and solution robustness of various hypothetical case studies. These tests reveal critical time horizons for expansion and the relative importance of material costs in driving the optimal sourcing scenario. Finally, a discussion on how to extend the programs is provided. The programs successfully account for the different nature of each cost driver; optimize according to the given constraints; and provide a fast, scriptable interface for parameter testing.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>