A simulation-based fuzzy chance-constrained programming model for optimal groundwater remediation under uncertainty

He, Li; Huang, Guohe; Lu, Hongwei

doi:10.1016/j.advwatres.2008.07.009

Cited by 68 publications

(26 citation statements)

References 53 publications

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“…Due to the complexities of multi-period mitigation guidelines, the mitigation rates may vary with the preference of local authorities, which would lead to various possibilities in nutrient load emission permits. For better reflecting the flexibility of the decisions, TN and TP emission permits can be treated as fuzzy sets (He et al 2008 (Gao et al 2014;Liu et al 2014). Accordingly, we set the TN emission permit for urban drainage as (7720, 7810, 7900), (6940,7030,7110), and (6250, 6325, 6400) tonnes/year during periods 1, 2, and 3, respectively.…”

Section: Case Studymentioning

confidence: 99%

A Generalized Interval Fuzzy Chance-Constrained Programming Method for Domestic Wastewater Management Under Uncertainty – A Case Study of Kunming, China

Dai

Cai

Liu

et al. 2015

Water Resour Manage

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In this study, interval mathematical programming (IMP), m λ -measure, and fuzzy chance-constrained programming are incorporated into a general optimization framework, leading to a generalized interval fuzzy chance-constrained programming (GIFCP) method. GIFCP can be used to address not only interval uncertainties in the objective function, variables and left-hand side parameters but also fuzzy uncertainties on the right-hand side. Also, it can reflect the aspiration preference of optimistic and pessimistic decision makers due to the integration of m λ -measure. The developed method is applied to the long-term planning of a domestic wastewater management system in the city of Kunming, China, with consideration of the eco-environmental protection of downstream water body. The solution results of the GIFCP method can generate a series of optimal wastewater allocation patterns and WTPs capacity expansion schemes under different risk levels, provide in-depth insights into the Water Resour Manage (2015) 29:3015-3036 effects of uncertainties, and consider the proper balance between system cost and risk of constraint violation.

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Section: Case Studymentioning

confidence: 99%

A Generalized Interval Fuzzy Chance-Constrained Programming Method for Domestic Wastewater Management Under Uncertainty – A Case Study of Kunming, China

Dai

Cai

Liu

et al. 2015

Water Resour Manage

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“…As a result, ITSP may become infeasible when the decision maker is risk-taking under high-variability conditions (Chen et al, 2013). The chance constrained programming (CCP) technique, which requires that the constraints are satisfied in a proportion of cases under given probability levels, is capable of addressing the above difficulty (He et al, 2008). Therefore, a potential approach is to integrate ITSP with CCP in a comprehensive framework of agricultural irrigation water and land-use resources allocation by fully considering the randomness and risk violations of water availability for agricultural systems.…”

Section: Introductionmentioning

confidence: 99%

An uncertainty-based framework for agricultural water-land resources allocation and risk evaluation

Guo

Singh

et al. 2016

Agricultural Water Management

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“…Recently, fuzzy and interval programming techniques have been used to address the uncertainty in parameters involving pollutant emissions, control techniques' efficiencies, operating costs, and environmental standards (He et al 2008a), as well as in stipulations associated with pollutant emission and ambient air quality (Liu et al 2003). For example, Liu et al (2003) proposed a fuzzy robust programming method and applied it to a case study for regional air quality management by incorporating the Gaussian dispersion model into its modeling framework to predict contaminant concentrations at receptor zones.…”

Section: Introductionmentioning

confidence: 99%

A Two-Phase Optimization Model Based on Inexact Air Dispersion Simulation for Regional Air Quality Control

Huang

2010

Water Air Soil Pollut

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This study proposes a two-phase optimization model for regional air pollution control. To predict the pollutant concentrations at receptor zones, an interval Gaussian plume model is advanced to facilitate the generation of optimal pollution control policies. Results from the case study indicate satisfactory performance of the proposed model in handling uncertainties in parameters expressed as intervals and in stipulations associated with pollutant emission and ambient air quality. Compared with conventional models, it has advantages of generating compromised management strategies according to decision makers' preference. This would be useful when the guarantee of satisfying all constraints is inapplicable or too costly. The proposed model is capable of identifying key factors and/or input conditions that may intensely affect system outputs and thus facilitating decision makers in adjusting current system status to benefit future management. The results also reveal a significantly enhanced satisfactory level would be obtained compared with conventional "singlephase"-based optimization models.

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A simulation-based fuzzy chance-constrained programming model for optimal groundwater remediation under uncertainty

Cited by 68 publications

References 53 publications

A Generalized Interval Fuzzy Chance-Constrained Programming Method for Domestic Wastewater Management Under Uncertainty – A Case Study of Kunming, China

A Generalized Interval Fuzzy Chance-Constrained Programming Method for Domestic Wastewater Management Under Uncertainty – A Case Study of Kunming, China

An uncertainty-based framework for agricultural water-land resources allocation and risk evaluation

A Two-Phase Optimization Model Based on Inexact Air Dispersion Simulation for Regional Air Quality Control

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