Title:
Algorithmic Framework for Improving Heuristics in Stochastic, Stage-Wise Optimization Problems

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dc.contributor.advisor Lee, Jay H.
dc.contributor.advisor Realff, Matthew J.
dc.contributor.author Choi, Jaein en_US
dc.contributor.committeeMember Ahmed, Shabbir
dc.contributor.committeeMember Ayhan, Hayriye
dc.contributor.committeeMember Bommarius, Aneas S.
dc.contributor.department Chemical Engineering en_US
dc.date.accessioned 2005-03-02T21:35:18Z
dc.date.available 2005-03-02T21:35:18Z
dc.date.issued 2004-11-24 en_US
dc.description.abstract Algorithmic Framework for Improving Heuristics in Stochastic, Stage-Wise Optimization Problems Jaein Choi 172 Pages Directed by Dr. Jay H. Lee and Dr. Matthew J. Realff The goal of this thesis is the development of a computationally tractable solution method for stochastic, stage-wise optimization problems. In order to achieve the goal, we have developed a novel algorithmic framework based on Dynamic Programming (DP) for improving heuristics. The propose method represents a systematic way to take a family of solutions and patch them together as an improved solution. However, patching is accomplished in state space, rather than in solution space. Since the proposed approach utilizes simulation with heuristics to circumvent the curse of dimensionality of the DP, it is named as Dynamic Programming in Heuristically Restricted State Space. The proposed algorithmic framework is applied to stochastic Resource Constrained Project Scheduling problems, a real-world optimization problem with a high dimensional state space and significant uncertainty equivalent to billions of scenarios. The real-time decision making policy obtained by the proposed approach outperforms the best heuristic applied in simulation stage to form the policy. The proposed approach is extended with the idea of Q-Learning technique, which enables us to build empirical state transition rules through simulation, for stochastic optimization problems with complicated state transition rules. Furthermore, the proposed framework is applied to a stochastic supply chain management problem, which has high dimensional action space as well as high dimensional state space, with a novel concept of implicit sub-action space that efficiently restricts action space for each state in the restricted state space. The resulting real-time policy responds to the time varying demand for products by stitching together decisions made by the heuristics and improves overall performance of the supply chain. The proposed approach can be applied to any problem formulated as a stochastic DP, provided that there are reasonable heuristics available for simulation. en_US
dc.description.degree Ph.D. en_US
dc.format.extent 9149223 bytes
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/4954
dc.language.iso en_US
dc.publisher Georgia Institute of Technology en_US
dc.subject Scheduling en_US
dc.subject Uncertainty
dc.subject Optimization
dc.subject Heuristics
dc.subject Supply chain management
dc.subject Dynamic programming
dc.title Algorithmic Framework for Improving Heuristics in Stochastic, Stage-Wise Optimization Problems en_US
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Realff, Matthew J.
local.contributor.corporatename School of Chemical and Biomolecular Engineering
local.contributor.corporatename College of Engineering
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relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
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