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Stephen C. Graves. Yuan, Rong, Ph. D. Massachusetts Institute of Technology Massachusetts Institute of Technology. Computation for Design and Optimization Program. Massachusetts Institute of Technology. Computation for Design and Optimization Program 2013-11-18T19:20:43Z 2013-11-18T19:20:43Z 2013 2013 http://hdl.handle.net/1721.1/82419 862818254 Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2013. Cataloged from PDF version of thesis. Includes bibliographical references (p. 73-75). In this research we model a job shop that produces a set of discrete parts in a make-to-stock setting. The intent of the research is to develop a planning model to determine the optimal operating tactics that minimize the relevant manufacturing costs subject to workload variability and capacity limits. We model the interplay of three key components in the job shop, namely, the production frequency for each part, the variability of production at each work station, and the level of parts inventory. We consider two operating tactics (decision variables): the production lot size for each part and the planned lead time for each work station. We model the relevant manufacturing costs, entailing production overtime costs and inventory-related costs (finished parts, work-in-process, and raw materials), as functions of these decision variables. We formulate a non-linear optimization model and implement it in the Excel Spreadsheet. We solve the model with the premium Excel Solver to determine the minimum-cost operating tactics. We test the model with both hypothetical and actual factory data from our research sponsor. The target factory processes 133 product parts on 59 work stations. The results are consistent with our intuition and demonstrate the potential value from optimizing over these tactics; these tests also provide some managerial insights on the application of these operating tactics. by Rong Yuan. S.M. 84 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 Computation for Design and Optimization Program. Setting optimal production lot sizes and planned lead times in a job shop system Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="357e9437-7cc3-453c-884f-0cc4d884b1eb"> <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>Setting optimal production lot sizes and planned lead times in a job shop system</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2013</PublicationDate> <Authors> <Author> <DisplayName>Yuan, Rong, Ph. D. 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>Computation for Design and Optimization Program.</Keyword> <Abstract>In this research we model a job shop that produces a set of discrete parts in a make-to-stock setting. The intent of the research is to develop a planning model to determine the optimal operating tactics that minimize the relevant manufacturing costs subject to workload variability and capacity limits. We model the interplay of three key components in the job shop, namely, the production frequency for each part, the variability of production at each work station, and the level of parts inventory. We consider two operating tactics (decision variables): the production lot size for each part and the planned lead time for each work station. We model the relevant manufacturing costs, entailing production overtime costs and inventory-related costs (finished parts, work-in-process, and raw materials), as functions of these decision variables. We formulate a non-linear optimization model and implement it in the Excel Spreadsheet. We solve the model with the premium Excel Solver to determine the minimum-cost operating tactics. We test the model with both hypothetical and actual factory data from our research sponsor. The target factory processes 133 product parts on 59 work stations. The results are consistent with our intuition and demonstrate the potential value from optimizing over these tactics; these tests also provide some managerial insights on the application of these operating tactics.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>