Operations Research
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
 QUICK SEARCH:   [advanced]


     


OPERATIONS RESEARCH
Vol. 55, No. 4, July-August 2007, pp. 792-798
DOI: 10.1287/opre.1060.0377
This Article
Right arrow Full Text (PDF)
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by An, H.
Right arrow Articles by Eheart, J. W.
Right arrow Search for Related Content

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

Hyunhee An, J. Wayland Eheart

McKinsey and Company, Seoul 100-101, Korea
Department of Civil and Environmental Engineering, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801

hyunhee_an{at}mckinsey.com
weheart{at}uiuc.edu

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.

Subject classifications: environment; air quality control; programming; joint chance constraint.
History: Received July 2003; revision received July 2006; accepted July 2006.







HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
Copyright © 2007 by INFORMS.