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Environmental Modeling & Assessment

, Volume 20, Issue 5, pp 535–548 | Cite as

Development of an Improved Fuzzy Robust Chance-Constrained Programming Model for Air Quality Management

  • Ye Xu
  • Guohe Huang
Article

Abstract

A new uncertain optimization technology, called as improved fuzzy robust chance-constrained programming (IFRCCP) model, was applied for a case study involving air quality management. IFRCCP model was an integration of fuzzy robust optimization and fuzzy chance-constrained programming (FCCP), which was originated from robust possibilistic programming (RPP) model and was an extended version of robust optimization (RO) from stochastic to fuzzy environment. It improved RPP model through incorporating predefined fuzzy violation variables into model and overcoming the limitations in adopting FCCP approach to tackle all fuzzy constraints without consideration of their differences. The existence of violation variables was useful in maintaining the characteristics of RO model and evaluating the trade-off between system economy and reliability. The case study considers a real air quality management system in Fengrun district of Tangshan city, China. The applied results indicated that IFRCCP was capable of providing a sketch of proposed management system and generating a variety of control alternatives as the decision-making base. The successful application of IFRCCP provided good demonstration for air quality management in other cities or other management fields.

Keywords

Robust optimization Trade-off Air quality management Fengrun district Uncertainty 

Notes

Acknowledgments

This research was supported by the National Natural Science Foundation of China (Grant No. 51208196) and the Fundamental Research Funds for the Central Universities (Grant No. 13QN26). The authors deeply appreciate the anonymous reviewers for their insightful comments and suggestions which contributed much to improving the manuscript.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.MOE Key Laboratory of Regional Energy Systems Optimization, Sino-Canada Resources and Environmental Research AcademyNorth China Electric Power UniversityBeijingChina
  2. 2.Faculty of Engineering and Applied ScienceUniversity of ReginaReginaCanada

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