An interval-parameter fuzzy nonlinear optimization model for stream water quality management under uncertainty

Qin, Xiaosheng; Guo, Huiqing; Zeng, Guangming; Chakma, A.; Huang, Yuefei

doi:10.1016/j.ejor.2006.03.053

Cited by 144 publications

(60 citation statements)

References 20 publications

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“…Emergency evacuation is essential once a nuclear power accident occurs as radioactive materials may leak, posing lethal effects on human health (Huang et al, 2006;Qin et al, 2007). China is experiencing a boom from the development of nuclear power sites, and a large number of nuclear power plants have been constructed or are going to be constructed.…”

Section: Introductionmentioning

confidence: 99%

Inexact Fuzzy Stochastic Chance Constraint Programming for Emergency Evacuation in Qinshan Nuclear Power Plant under Uncertainty

Huang¹,

Nie²,

Guo³

et al. 2017

J ENVIRON INFORM

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ABSTRACT. Nuclear power accidents are one of the most dangerous disasters posing a lethal threat to human health and have detrimental effects lasting for decades. Therefore, emergency evacuation is important to minimize injuries and prevent lethal consequences resulting from a nuclear power accident. An inexact fuzzy stochastic chance constrained programming (IFSCCP) method is developed to address various uncertainties in evacuation management problems. It integrates the interval-parameter programming (IPP) and fuzzy stochastic chance constrained programming (FSCCP) methods into a general framework, in which the IPP method addresses the uncertainties presented as intervals defined by crisp lower and upper bounds, and the FCCP treat the dual-uncertainties expressed as fuzzy random variables. The measures of possibility and necessity were employed to convert the fuzzy random variables into crisp values to reflect the decision maker's pessimistic and optimistic preferences. The IFSCCP model is applied to support nuclear emergency evacuation management in the Qinshan Nuclear Power Site, which is one of the largest nuclear plants in China. The results provide stable intervals for the objective function and decision variables with different fuzzy and probability confidence levels regarding the local residents' distribution. Nine scenarios are analyzed to reflect the impacts of the imprecision (fuzziness and randomness) associated with the size of the population in a plume emergency planning zone. The results are valuable for supporting local decision makers to generate effective emergency evacuation strategies.

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Section: Introductionmentioning

confidence: 99%

Inexact Fuzzy Stochastic Chance Constraint Programming for Emergency Evacuation in Qinshan Nuclear Power Plant under Uncertainty

Huang¹,

Nie²,

Guo³

et al. 2017

J ENVIRON INFORM

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show abstract

“…Moreover, subjective judgments obtained from experts and stakeholders also exert significant impacts on data acquisition and system reliability. These complexities lead to the difficulties in solving the resulted uncertain optimization problems (Azaiez et al, 2005;Sethi et al, 2006;Tan et al, 2011;Bender and Simonovic, 2000;Guo et al, 2010;Qin et al, 2007;Lu et al, 2012;Huang et al, 2012;Zhang et al, 2009;Gu et al, 2013;Dessai and Hulme, 2007;Cai et al, 2011Cai et al, , 2012Li et al, 2013). Nowadays, the stochastic linear programming (SLP) and interval linear programming (ILP) have become two of the most effective optimization approaches, especially the chance-constraints programming (CCP).…”

Section: Introductionmentioning

confidence: 99%

A dual-inexact fuzzy stochastic model for water resources management and non-point source pollution mitigation under multiple uncertainties

Dong

Tan

Huang

et al. 2014

Hydrol. Earth Syst. Sci.

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Abstract. In this research, a dual-inexact fuzzy stochastic programming (DIFSP) method was developed for supporting the planning of water and farmland use management system considering the non-point source pollution mitigation under uncertainty. The random boundary interval (RBI) was incorporated into DIFSP through integrating fuzzy linear programming (FLP) and chance-constrained programming (CCP) approaches within an interval linear programming (ILP) framework. This developed method could effectively tackle the uncertainties expressed as intervals and fuzzy sets. Moreover, the lower and upper bounds of RBI are continuous random variables, and the correlation existing between the lower and upper bounds can be tackled in RBI through the joint probability distribution function. And thus the subjectivity of decision making is greatly reduced, enhancing the stability and robustness of obtained solutions. The proposed method was then applied to solve a water and farmland use planning model (WFUPM) with non-point source pollution mitigation. The generated results could provide decision makers with detailed water supply-demand schemes involving diversified water-related activities under preferred satisfaction degrees. These useful solutions could allow more in-depth analyses of the trade-offs between humans and environment, as well as those between system optimality and reliability. In addition, comparative analyses on the solutions obtained from ICCP (Interval chance-constraints programming) and DIFSP demonstrated the higher application of this developed approach for supporting the water and farmland use system planning.

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“…Some studies further incorporated probability theory into IFMP to deal with right-side uncertainties of the constraints (Li et al, 2006;He et al, 2008;Li et al, 2009;Guo and Huang, 2011;Wang et al, 2012). These mathematical programming techniques mentioned above are also applied to investigate various planning issues such as water resource planning and management (Qin et al, 2007;Zhu et al, 2009;, energy system planning and management (Zhu et al, 2012;Dong et al, 2012), and air quality management (Xu et al, , 2012, etc.…”

Section: Introductionmentioning

confidence: 99%

Compromising economic cost and air pollutant emissions of municipal solid waste management strategies by fuzzy multiobjective optimization model

Chang

Lin

2013

Journal of the Air & Waste Management Association

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Municipal solid waste management (MSWM) is an important environmental challenge and subject in urban planning. A sustainable MSWM strategy should consider not only economic efficiency but also life-cycle assessment of environmental impact. This study employs the fuzzy multiobjective linear programming (FMOLP) technique to find the optimal compromise between economic optimization and pollutant emission reduction for the MSWM strategy. Taichung City in Taiwan is evaluated as a case study. The results indicate that the optimal compromise MSWM strategy can reduce significant amounts of pollutant emissions and still achieve positive net profits. Minimization of the sulfur oxide (SO x ) and nitrogen oxide (NO x ) emissions are the two major priorities in achieving this optimal compromise strategy when recyclables recovery rate is lower; however, minimization of the carbon monoxide (CO) and particulate matter (PM) emissions become priority factors when recovery rate is higher.Implications: This paper applied the multiobjective optimization model to find the optimal compromise municipal solid waste management (MSWM) strategy, which minimizes both life-cycle operating cost and air pollutant emissions, and also to analyze the correlation between recyclables recovery rates and optimal compromise strategies. It is different from past studies, which only consider economic optimization or environmental impacts of the MSWM system. The result shows that optimal compromise MSWM strategy can achieve a net profit and reduce air pollution emission. In addition, scenario investigation of recyclables recovery rates indicates that resource recycling is beneficial for both economic optimization and air pollutant emission minimization.

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An interval-parameter fuzzy nonlinear optimization model for stream water quality management under uncertainty

Cited by 144 publications

References 20 publications

Inexact Fuzzy Stochastic Chance Constraint Programming for Emergency Evacuation in Qinshan Nuclear Power Plant under Uncertainty

Inexact Fuzzy Stochastic Chance Constraint Programming for Emergency Evacuation in Qinshan Nuclear Power Plant under Uncertainty

A dual-inexact fuzzy stochastic model for water resources management and non-point source pollution mitigation under multiple uncertainties

Compromising economic cost and air pollutant emissions of municipal solid waste management strategies by fuzzy multiobjective optimization model

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