Efficient management of air quality considering fuzzy confidences with varied reliabilities

Regional air quality management systems are complicated by uncertainties due to their interactive, dynamic, and multi-objective features. In this study, an inexact double-sided fuzzy chance-constrained programming (IDFCCP) model was developed and applied to a hypothetical case of regional air quality management. The results indicated that the proposed IDFCCP improved upon the existing ILP and DFCCP approaches; the fuzzy confidences at different levels could be analysed with varied reliability scenarios, making it possible to handle fuzzy uncertainties originating from both sides of the model constraints; other uncertain parameters could be expressed in terms of discrete intervals. The trade-off between system economy and reliability could be analysed by decision makers according to their preferences. The study results demonstrated that the proposed method could help decision makers identify desired policies under various environmental, economic, system-feasibility and system-reliability constraints.

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