PMID: 20102036Jan 28, 2010Paper

A genetic-algorithm-aided stochastic optimization model for regional air quality management under uncertainty

Journal of the Air & Waste Management Association
Xiaosheng QinLei Liu

Abstract

A genetic-algorithm-aided stochastic optimization (GASO) model was developed in this study for supporting regional air quality management under uncertainty. The model incorporated genetic algorithm (GA) and Monte Carlo simulation techniques into a general stochastic chance-constrained programming (CCP) framework and allowed uncertainties in simulation and optimization model parameters to be considered explicitly in the design of least-cost strategies. GA was used to seek the optimal solution of the management model by progressively evaluating the performances of individual solutions. Monte Carlo simulation was used to check the feasibility of each solution. A management problem in terms of regional air pollution control was studied to demonstrate the applicability of the proposed method. Results of the case study indicated the proposed model could effectively communicate uncertainties into the optimization process and generate solutions that contained a spectrum of potential air pollutant treatment options with risk and cost information. Decision alternatives could be obtained by analyzing tradeoffs between the overall pollutant treatment cost and the system-failure risk due to inherent uncertainties.

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Citations

Mar 8, 2012·Journal of the Air & Waste Management Association·Yurui FanAmornvadee Veawab
Mar 12, 2013·Journal of the Air & Waste Management Association·S Wang, G H Huang
Aug 5, 2010·Journal of the Air & Waste Management Association·Ye XuXiaosheng Qin
Sep 15, 2015·The Science of the Total Environment·Guanhui ChengCong Dong
Jul 4, 2018·Environmental Science and Pollution Research International·Armando Di NardoDino Musmarra
Jun 2, 2020·Journal of the Air & Waste Management Association·Chenghang ZhengXiang Gao

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