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A Decision-Making Framework for Ozone Pollution Control

Author

Listed:
  • 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)

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.

Suggested Citation

  • Zehua Yang & Victoria C. P. Chen & Michael E. Chang & Melanie L. Sattler & Aihong Wen, 2009. "A Decision-Making Framework for Ozone Pollution Control," Operations Research, INFORMS, vol. 57(2), pages 484-498, April.
    • Handle: RePEc:inm:oropre:v:57:y:2009:i:2:p:484-498
    DOI: 10.1287/opre.1080.0576
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    References listed on IDEAS

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    1. S. A. Murphy, 2003. "Optimal dynamic treatment regimes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 331-355, May.
    2. Aziz Bouzaher & John B. Braden & Gary V. Johnson, 1990. "A Dynamic Programming Approach to a Class of Nonpoint Source Pollution Control Problems," Management Science, INFORMS, vol. 36(1), pages 1-15, January.
    3. Sharon A. Johnson & Jery R. Stedinger & Christine A. Shoemaker & Ying Li & José Alberto Tejada-Guibert, 1993. "Numerical Solution of Continuous-State Dynamic Programs Using Linear and Spline Interpolation," Operations Research, INFORMS, vol. 41(3), pages 484-500, June.
    4. Seinfeld, John H. & Kyan, Chwan P., 1971. "Determination of optimal air pollution control strategies," Socio-Economic Planning Sciences, Elsevier, vol. 5(3), pages 173-190, June.
    5. Julia Tsai & Victoria Chen & M. Beck & Jining Chen, 2004. "Stochastic Dynamic Programming Formulation for a Wastewater Treatment Decision-Making Framework," Annals of Operations Research, Springer, vol. 132(1), pages 207-221, November.
    6. Christine A. Shoemaker, 1982. "Optimal Integrated Control of Univoltine Pest Populations with Age Structure," Operations Research, INFORMS, vol. 30(1), pages 40-61, February.
    7. Victoria C. P. Chen & David Ruppert & Christine A. Shoemaker, 1999. "Applying Experimental Design and Regression Splines to High-Dimensional Continuous-State Stochastic Dynamic Programming," Operations Research, INFORMS, vol. 47(1), pages 38-53, February.
    8. Cervellera, Cristiano & Chen, Victoria C.P. & Wen, Aihong, 2006. "Optimization of a large-scale water reservoir network by stochastic dynamic programming with efficient state space discretization," European Journal of Operational Research, Elsevier, vol. 171(3), pages 1139-1151, June.
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    Cited by:

    1. Huiyuan Fan & Prashant K. Tarun & Victoria C. P. Chen & Dachuan T. Shih & Jay M. Rosenberger & Seoung Bum Kim & Robert A. Horton, 2018. "Data-driven optimization for Dallas Fort Worth International Airport deicing activities," Annals of Operations Research, Springer, vol. 263(1), pages 361-384, April.
    2. Dachuan Shih & Seoung Kim & Victoria Chen & Jay Rosenberger & Venkata Pilla, 2014. "Efficient computer experiment-based optimization through variable selection," Annals of Operations Research, Springer, vol. 216(1), pages 287-305, May.
    3. Tajbakhsh, Alireza & Hassini, Elkafi, 2022. "A game-theoretic approach for pollution control initiatives," International Journal of Production Economics, Elsevier, vol. 254(C).
    4. Ariyajunya, Bancha & Chen, Ying & Chen, Victoria C.P. & Kim, Seoung Bum & Rosenberger, Jay, 2021. "Addressing state space multicollinearity in solving an ozone pollution dynamic control problem," European Journal of Operational Research, Elsevier, vol. 289(2), pages 683-695.
    5. Xiaotong Sun & Wei Xu & Hongxun Jiang & Qili Wang, 2021. "A deep multitask learning approach for air quality prediction," Annals of Operations Research, Springer, vol. 303(1), pages 51-79, August.

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