A Decision-Making Framework for Ozone Pollution Control
成果类型：
Article
署名作者：
Yang, Zehua; Chen, Victoria C. P.; Chang, Michael E.; Sattler, Melanie L.; Wen, Aihong
署名单位：
Abbott Laboratories; University of Texas System; University of Texas Arlington; University System of Georgia; Georgia Institute of Technology; University of Texas System; University of Texas Arlington
刊物名称：
OPERATIONS RESEARCH
ISSN/ISSBN：
0030-364X
DOI：
10.1287/opre.1080.0576
发表日期：
2009
页码：
484-498
关键词：
摘要：
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.