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Analysis of batching strategies for multi-item production with yield uncertainty

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dc.contributor.advisor Stephen C. Graves. en_US Siow, Christopher (Christopher Shun Yi) en_US
dc.contributor.other Massachusetts Institute of Technology. Operations Research Center. en_US 2008-11-07T19:01:34Z 2008-11-07T19:01:34Z 2008 en_US 2008 en_US
dc.description Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2008. en_US
dc.description Includes bibliographical references (p. 179-180). en_US
dc.description.abstract In this thesis, we investigate the batch sizing problem for a custom-job production facility. More specifically, given a production system that has been assigned several different types of custom jobs, we try to derive batching policies to minimize the expected total time that a job spends in the system. Custom-job production brings a host of challenges that makes batch sizing very difficult - production can only begin when the order arrives, the yield uncertainty probabilities are fairly large, and the production quantities are typically small. Furthermore, deriving an optimal batch sizing policy is difficult due to the heterogeneity of the job types; each job type has a different demand, batch setup time, unit production rate, unit defective probability, and job arrival rate. In addition, further complexity stems from the fact that the batch sizing decisions for each job type are coupled, and cannot be made independently. Given the difficulties in selecting the batch sizes, we propose an alternative batching method that minimizes the system utilization instead of the expected total job time. The main advantage of this approach is that is allows us to choose the batch size of each job type individually. First, we model the system as an M/G/l queue, and obtain a closed-form expression for the expected total job time when the demand is restricted to be a single unit. Following which, we show empirically that the minimum utilization heuristic attains near-optimal performance under the unit demand restriction. We then build on this analysis, and extend the heuristic to the general case in which the demand of each job is allowed to be more than a single unit. Finally, we use simulations to compare our heuristic against other alternative batching policies, and the results indicate that our heuristic is indeed an effective strategy. en_US
dc.description.statementofresponsibility by Christopher Siow. en_US
dc.format.extent 180 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights 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. en_US
dc.rights.uri en_US
dc.subject Operations Research Center. en_US
dc.title Analysis of batching strategies for multi-item production with yield uncertainty en_US
dc.type Thesis en_US S.M. en_US
dc.contributor.department Massachusetts Institute of Technology. Operations Research Center. en_US
dc.identifier.oclc 244442883 en_US

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