Branch-and-price methods for prescribing profitable upgrades of high-technology products with deterministic or stochastic demands /
This dissertation deals with a model that would help manufacturers make profitable decisions in upgrading the features of a family (i.e., set) of related high-technology products over their life cycle. The model integrates various organizations in an enterprise: product design, manufacturing, produ...
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| Format: | Thesis Book |
| Language: | English |
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[Place of publication not identified] :
[publisher not identified] ;
2002.
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| Online Access: | http://proxy.library.tamu.edu/login?url=http://proquest.umi.com/pqdweb?did=726460251&sid=1&Fmt=2&clientId=2945&RQT=309&VName=PQD |
| Summary: | This dissertation deals with a model that would help manufacturers make profitable decisions in upgrading the features of a family (i.e., set) of related high-technology products over their life cycle. The model integrates various organizations in an enterprise: product design, manufacturing, production planning, and supply chain management. The research objectives are as follows: (1) a branch-and-price approach to solve the deterministic model in which all parameters are known with certainty, (2) stochastic models to deal with random customer demands following both the non-sequential and sequential approaches, (3) a branch-and-price approach to solve the deterministic equivalents of the stochastic models, (4) deterministic approximations to the non-sequential model for some special cases, and (5) a comparison of the effectiveness of our branch-and-price approach with that of commercial software on the basis of run time. Uncertainties in customer demand are modeled via a finite number of scenarios. This multi-period stochastic problem is addressed using both non-sequential and sequential approaches. Branch-and-price solution approaches are devised to effectively solve the deterministic and stochastic problems effectively. Sets of random instances are generated to evaluate the effectiveness of our solution approach in comparison with that of commercial software on the basis of run time. Computational results indicate that our approach outperforms commercial software on all of our test problems and is capable of solving large problems in reasonable time. Several examples are presented to demonstrate how managers could use our models to answer several "what if" questions. Special cases for which the non-sequential stochastic problem can be replaced by a deterministic approximation are identified. A bound on the relative error, which results from the deterministic approximation, is derived for each special case. Bounds on the relative errors are shown to be acceptable. The purpose of this dissertation is to devise effective solution procedures to solve stochastic versions of the upgrading problem. These results demonstrate that our solution approaches are successful and suggest that these approaches can also be applied successfully to other stochastic problems. |
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| Item Description: | Vita. "Major Subject: Industrial Engineering". |
| Physical Description: | xi, 158 leaves : illustrations ; 28 cm. Issued also on microfiche from University Microfilm Inc. |
| Bibliography: | Includes bibliographical references (leaves 152-157). |