Development of an Improved Fuzzy Robust Chance-Constrained Programming Model for Air Quality Management
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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.
KeywordsRobust optimization Trade-off Air quality management Fengrun district Uncertainty
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.
- 3.Guldman, J. M. (1988). Chance-constrained dynamic model of air quality management. Fuzzy Sets and Systems, 114(5), 1116–1126.Google Scholar
- 5.Xu, T. Y., & Qin, X. S. (2014). Integrating decision analysis with fuzzy programming: application in urban water distribution system operation. Journal of Water Resources Planning and Management (ASCE), 140(5), 638--648.Google Scholar
- 11.Xi, B. D., Qin, X. S., Su, X. K., Jiang, Y. H., & Wei, Z. M. (2008). Characterizing effects of uncertainties in MSW composting process through a coupled fuzzy vertex and factorial-analysis approach. Waste Management, 28(9), 1609-1623.Google Scholar
- 21.Qin, X. S. (2012). Assessing environmental risks through fuzzy parameterized probabilistic analysis. Stochastic Environmental Research and Risk Assessment, 26(1), 43--58.Google Scholar
- 22.Xu, T. Y., & Qin, X. S. (2013). Solving water quality management problem through combined genetic algorithm and fuzzy simulation. Journal of Environmental Informatics, 22(1), 39--48.Google Scholar