In:
Operations Research, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 57, No. 2 ( 2009-04), p. 484-498
Abstract:
In this paper, an intelligent decision-making framework (DMF) is developed to help decision makers identify cost-effective ozone control policies. High concentrations of ozone at the ground level continue to be a serious problem in numerous U.S. cities. Our DMF searches for dynamic and targeted control policies that require a lower total reduction of emissions than current control strategies based on the “trial and error” approach typically employed by state government decision makers. Our DMF utilizes a rigorous stochastic dynamic programming (SDP) formulation and incorporates an atmospheric chemistry module to model how ozone concentrations change over time. Within the atmospheric chemistry module, methods from design and analysis of computer experiments are employed to create SDP state transition equation metamodels, and critical dimensionality reduction is conducted to reduce the state-space dimension in solving our SDP problem. Results are presented from a prototype DMF for the Atlanta metropolitan region.
Type of Medium:
Online Resource
ISSN:
0030-364X
,
1526-5463
DOI:
10.1287/opre.1080.0576
Language:
English
Publisher:
Institute for Operations Research and the Management Sciences (INFORMS)
Publication Date:
2009
detail.hit.zdb_id:
2019440-7
detail.hit.zdb_id:
123389-0
SSG:
3,2
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