A Decision-Making Framework for Ozone Pollution Control

Zehua Yang (), Victoria C. P. Chen (), Michael E. Chang (), Melanie L. Sattler () and Aihong Wen ()
Additional contact information
Zehua Yang: Abbott Laboratories, Irving, Texas 75038
Victoria C. P. Chen: Department of Industrial and Manufacturing Systems Engineering, The University of Texas at Arlington, Arlington, Texas 76019
Michael E. Chang: School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332
Melanie L. Sattler: Department of Civil Engineering, The University of Texas at Arlington, Arlington, Texas 76019
Aihong Wen: PROS Revenue Management, Houston, Texas 77002

Operations Research, 2009, vol. 57, issue 2, 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.

Keywords: environment; dynamic programming; applications; statistics; data analysis (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (6)

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