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dc.contributor.advisorJung-Hoon Chun and Stephen Graves.en_US
dc.contributor.authorPáez, Daýanen_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Mechanical Engineering.en_US
dc.date.accessioned2010-09-02T17:23:38Z
dc.date.available2010-09-02T17:23:38Z
dc.date.copyright2010en_US
dc.date.issued2010en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/58288
dc.descriptionThesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2010.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (p. 93).en_US
dc.description.abstractA 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.en_US
dc.description.statementofresponsibilityby Daýan Páez.en_US
dc.format.extent142 p.en_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.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.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectMechanical Engineering.en_US
dc.titleDiscrete, recursive supply chain model for solar panel manufacturingen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineering
dc.identifier.oclc650085443en_US


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