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dc.contributor.advisorDonald Rosenfield and Bruce G. Cameron.en_US
dc.contributor.authorGorang, Brandon Paulen_US
dc.contributor.otherLeaders for Global Operations Program.en_US
dc.date.accessioned2016-09-27T15:15:09Z
dc.date.available2016-09-27T15:15:09Z
dc.date.copyright2016en_US
dc.date.issued2016en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/104398
dc.descriptionThesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2016. In conjunction with the Leaders for Global Operations Program at MIT.en_US
dc.descriptionThesis: S.M. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2016. In conjunction with the Leaders for Global Operations Program at MIT.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 83-84).en_US
dc.description.abstractThis thesis addresses the allocation of gas turbine aircraft engines to maintenance facilities. Scheduling a global engine maintenance network can be very complex and challenging. This project pertains particularly to the V2500 IAE engine maintenance network managed by Pratt & Whitney. Using a mathematical program to automate engine allocation was believed to reduce the workload on the organization and the cost of maintaining the 3100 engine fleet. An introduction to the engine maintenance network will be covered along with an explanation of Fleet Hour Agreements (FHA). A literature review of mathematical programming is included to provide background of pertinent information. The current state of the business is analyzed. An integer linear program is developed to closely represent the current state of the business. Historical data was used to feed the model, and the outputs from the model were compared to actuals. A sensitivity analysis is performed to better understand the constraints of the current business and the feasibility of the model. An optimization model should not be used to plan engine maintenance given the current state of business. The business is too dynamic and the network is highly constrained by capacity. The results also show a much smaller savings than were originally expected. This is mostly due to better understanding the cost of maintaining the engines at the different shops. The variation was much lower than originally expected. The current state is operating close to optimal with great flexibility and should continue on as is.en_US
dc.description.statementofresponsibilityby Brandon Paul Gorang.en_US
dc.format.extent102 pagesen_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.subjectSloan School of Management.en_US
dc.subjectInstitute for Data, Systems, and Society.en_US
dc.subjectEngineering Systems Division.en_US
dc.subjectLeaders for Global Operations Program.en_US
dc.titleScheduling a global engine maintenance networken_US
dc.typeThesisen_US
dc.description.degreeM.B.A.en_US
dc.description.degreeS.M. in Engineering Systemsen_US
dc.contributor.departmentLeaders for Global Operations Program at MITen_US
dc.contributor.departmentMassachusetts Institute of Technology. Engineering Systems Division
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Data, Systems, and Society
dc.contributor.departmentSloan School of Management
dc.identifier.oclc958269717en_US


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