A Decision-Making Framework for Ozone Pollution Control

Authors:
Zehua Yang;Victoria C. P. Chen;Michael E. Chang;Melanie L. Sattler;Aihong Wen
Affiliations:
Abbott Laboratories, Irving, Texas 75038;Department of Industrial and Manufacturing Systems Engineering, The University of Texas at Arlington, Arlington, Texas 76019;School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332;Department of Civil Engineering, The University of Texas at Arlington, Arlington, Texas 76019;PROS Revenue Management, Houston, Texas 77002
Venue:
Operations Research
Year:
2009

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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.