A chance-constrained multi-period model for a special multi-reservoir system

Azaiez, Mohamed Naceur; Hariga, Moncer; Al-Harkan, Ibrahim M.

doi:10.1016/j.cor.2003.11.009

Cited by 50 publications

(16 citation statements)

References 24 publications

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“…Various optimization techniques have been used to solve the problems of water resources (Morel-Seytoux 1975;Yaron and Dinar 1982;Ahlfeld et al 1988;Azaiez et al 2005;Katsifarakis and Petala 2006;Gaur et al 2011;Singh 2014d). The techniques used include linear programming (LP) (Castle and Lindeborg 1960;Vedula and Roger 1981;Feinerman and Yaron 1983;Peralta et al 1995;Mantoglou 2003), non-linear programming (NLP) (Rydzewski and Rashid 1981;Takahashi and Peralta 1995;Montazar et al 2010;Shamir et al 1984;Mantoglou and Papantoniou 2008), dynamic programming (DP) (Burt 1970;Yakowitz 1982;Lee and Kitanidis 1991;Datta and Dhiman 1996;Philbrick and Kitanidis 1998;Shangguan et al 2002;Tran et al 2011), quadratic programming (QP) (Lefkoff and Gorelick 1986), and genetic algorithm (GA) (Holland 1975;Sharif and Wardlaw 2000;Maskey et al 2002;Wardlaw and Bhaktikul 2004;Haq et al 2008;Wu et al 2007;Liu et al 2008;Rana et al 2008;Safavi et al 2009).…”

Section: Optimization Modelingmentioning

confidence: 99%

Review: Computer-based models for managing the water-resource problems of irrigated agriculture

Singh

2015

Hydrogeol J

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Irrigation is essential for achieving food security to the burgeoning global population but unplanned and injudicious expansion of irrigated areas causes waterlogging and salinization problems. Under this backdrop, groundwater resources management is a critical issue for fulfilling the increasing water demand for agricultural, industrial, and domestic uses. Various simulation and optimization approaches were used to solve the groundwater management problems. This paper presents a review of the individual and combined applications of simulation and optimization modeling for the management of groundwater-resource problems associated with irrigated agriculture. The study revealed that the combined use of simulation-optimization modeling is very suitable for achieving an optimal solution for groundwater-resource problems, even with a large number of variables. Independent model tools were used to solve the problems of uncertainty analysis and parameter estimation in groundwater modelling studies. Artificial neural networks were used to minimize the problem of computational complexity. The incorporation of socioeconomic aspects into the groundwater management modeling would be an important development in future studies.

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Section: Optimization Modelingmentioning

confidence: 99%

Review: Computer-based models for managing the water-resource problems of irrigated agriculture

Singh

2015

Hydrogeol J

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“…Edirisinghe et al (2000) advanced a chance-constrained programming model for the capacity planning of a multipurpose reservoir under random stream flows. Azaiez et al (2005) developed a CCP model for planning multireservoir operation under a conjunctive use of ground and surface water over a multi-period context, where the inf1ows to the main reservoir as well as the irrigation demands are treated by chance constraints. Qin and Huang (2009) proposed an inexact chanceconstrained quadratic programming method for stream water quality management, where uncertainties characterized as random variables with normal probability distributions and expressed as discrete intervals with knowing lower-and upper-bounds could be reflected.…”

Section: Introductionmentioning

confidence: 99%

Optimization of regional economic and environmental systems under fuzzy and random uncertainties

Huang

Nie

2011

Journal of Environmental Management

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“…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%

“…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). For instance, Azaiez et al (2005) tackled the uncertainties in inflows through adopting chance constraints and penalties of failure for optimal multi-period operation of a multi-reservoir system. And in 2006, Sethi et al (2006) developed deterministic linear programming (DLP) and chance-constrained linear programming (CCLP) models to allocate available land and water resources optimally on seasonal basis.…”

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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A chance-constrained multi-period model for a special multi-reservoir system

Cited by 50 publications

References 24 publications

Review: Computer-based models for managing the water-resource problems of irrigated agriculture

Review: Computer-based models for managing the water-resource problems of irrigated agriculture

Optimization of regional economic and environmental systems under fuzzy and random uncertainties

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

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