Stochastic dynamic programming with localized cost-to-go approximators - Application to large scale supply chain management under demand uncertainty

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A novel optimization algorithmic framework based on dynamic programming is proposed for solving multi-product supply chain management problems with manufacturing and distribution decisions under demand uncertainty. To generate reliable suboptimal policy for simulation and restricted state space identification, a deterministic mathematical programming (MILP) approach is utilized. The simulation data with fixed action profiles obtained from the MILPs with different demand patterns is directly utilized for real-time decision making with initial 'profit-to-go' values.
Publisher
INST CHEMICAL ENGINEERS
Issue Date
2005-06
Language
English
Article Type
Article; Proceedings Paper
Citation

CHEMICAL ENGINEERING RESEARCH DESIGN, v.83, no.6, pp.752 - 758

ISSN
0263-8762
DOI
10.1205/cherd.04375
URI
http://hdl.handle.net/10203/88063
Appears in Collection
CBE-Journal Papers(저널논문)
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