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A Screening Technique for Joint Chance-Constrained Programming for Air-Quality Management

Author

Listed:
  • Hyunhee An

    (McKinsey and Company, Seoul 100-101, Korea)

  • J. Wayland Eheart

    (Department of Civil and Environmental Engineering, University of Illinois at Urbana--Champaign, Urbana, Illinois 61801)

Abstract

This technical note presents a screening technique for using chance-constrained programming to achieve overall system (i.e., joint) reliability when there is statistical dependence between constraints representing an ambient air-quality requirement at different geographical locations. The technique is intended to determine whether the full analysis of row interdependence, which requires more intensive programming and larger computational effort, is warranted, by examining a possible spectrum of solutions at three extreme cases of row dependence. The technique is illustrated for airborne particulate emissions control, in which the overall cost of controlling particulate emissions from two electrostatic precipitators is minimized in a manner that maintains ground-level particulate concentration at all receptors with a prescribed reliability.In accordance with the theory presented here, such screening is achieved by setting the required reliability values of individual constraints according to assumptions of complete codependence, zero codependence, and complete negative codependence. In application, these reliability values represent the probability of achieving the ambient concentration standard at several receptor locations. The results of the screening technique are compared to those of two more computationally intensive methods for achieving overall system reliability. It is found that, for a given example, the screening technique brackets the results of those full-analysis methods for row dependence, as expected.

Suggested Citation

  • Hyunhee An & J. Wayland Eheart, 2007. "A Screening Technique for Joint Chance-Constrained Programming for Air-Quality Management," Operations Research, INFORMS, vol. 55(4), pages 792-798, August.
    • Handle: RePEc:inm:oropre:v:55:y:2007:i:4:p:792-798
    DOI: 10.1287/opre.1060.0377
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    References listed on IDEAS

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    1. Watanabe, Tsunemi & Ellis, Hugh, 1994. "A joint chance-constrained programming model with row dependence," European Journal of Operational Research, Elsevier, vol. 77(2), pages 325-343, September.
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    Cited by:

    1. Tanner, Matthew W. & Ntaimo, Lewis, 2010. "IIS branch-and-cut for joint chance-constrained stochastic programs and application to optimal vaccine allocation," European Journal of Operational Research, Elsevier, vol. 207(1), pages 290-296, November.
    2. Grani A. Hanasusanto & Vladimir Roitch & Daniel Kuhn & Wolfram Wiesemann, 2017. "Ambiguous Joint Chance Constraints Under Mean and Dispersion Information," Operations Research, INFORMS, vol. 65(3), pages 751-767, June.

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