A Genetic-Algorithm-Aided Stochastic Optimization Model for Regional Air Quality Management under Uncertainty

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 chanceconstrained programming (CCP) framework and allowed uncertainties in simulation and optimization model parameters to be considered explicitly in the design of leastcost 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.

A Multi-Objective Chance-Constrained Programming Approach for Uncertainty-Based Optimal Nutrients Load Reduction at the Watershed Scale

“…The conventional approach for transforming CCP is to translate the chance constraints into the approximated linear forms, and then the linear forms can be solved by traditional mathematical Water 2017, 9, 322 4 of 18 models [25][26][27]. For objective functions in linear form and some specific nonlinear functions in which parameters follow a normal distribution or an exponential distribution, approximated linear forms can be obtained, and CCP can be transformed into its relevant deterministic programming [28,29].…”

An Integrated Simulation-Assessment Approach for Evaluating Health Risks of Groundwater Contamination Under Multiple Uncertainties

“…The general formulation of risk assessment includes identification of sources of risk agents and their fate and transport through porous media, estimation of human exposure doses, and conversion of such exposures into risk levels (Liu et al 2004). However, the insight about risk is limited by the randomness inherent in nature and the lack of sufficient information related to the chances of risk occurrence and the potential consequences of such occurrence (Li et al 2007;Xu et al 2009a;Qin et al 2010). As a result, risk assessment is inherently linked with uncertainty and negligence of such uncertainty in the assessment procedures would bring biased or even false information to the related site managers and eventually harm the appropriateness of the final remediation decisions.…”

Mathematical modeling for air quality management under stochastic and interval uncertainties

“…Previously, a significant number of optimization techniques were developed for dealing with environmental management problems, including stochastic mathematical programming (SMP), fuzzy mathematical programming (FMP) and interval mathematical programming (IMP), as well as their integrations (Macchiato et al 1994;Teng and Tzeng 1994;Lejano et al 1997;Liu et al 2003;Zhu et al 2009;Cao et al 2010;Qin et al 2010;Xu et al 2010a;Fan et al 2012;Hu et al 2012;lv et al 2012;Xu et al 2012;Li et al 2014). For example, Liu et al (2003) developed a hybrid fuzzy-stochastic robust programming method for regional air quality management where the random and fuzzy variables are tackled by stochastic chance-constrained programming and fuzzy robust programming, respectively.…”