An interval-parameter multi-stage stochastic programming model for water resources management under uncertainty

Li, Y. P.; Huang, Guohe; Nie, Songlin

doi:10.1016/j.advwatres.2005.07.008

Cited by 245 publications

(137 citation statements)

References 37 publications

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“…20 -22 In MSP, decision variables are divided into two subsets: those that must be determined before the realizations of random variables are disclosed and those (recourse variables) that can be determined after the random variable values are available. In the past decades, several MSP methods were developed and applied to environmental management and energy systems planning [22][23][24][25] ; unfortunately, few applications of MSP to waste management were reported. The chanceconstrained programming (CCP) method can effectively reflect the reliability of satisfying (or risk of violating) system constraints under uncertainty.…”

Section: Discussionmentioning

confidence: 99%

“…[25][26][27][28] In model 1, uncertainties can be conceptualized into the scenario tree, with a one-to-one correspondence between the previous random variable and one of the nodes (state of the system) in each stage. 22 However, randomness in other parameters (e.g., waste management facility capacities) also needs to be reflected. The CCP method can be used for dealing with this type of uncertainty and analyzing the risk of violating the uncertain constraints.…”

Section: Methodsmentioning

confidence: 99%

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Modeling Municipal Solid Waste Management System under Uncertainty

Huang

2010

Journal of the Air & Waste Management Association

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In this study, a dynamic inexact waste management (DIWM) model is developed for identifying optimal waste-flow-allocation and facility-capacity-expansion strategies under uncertainty and is based on an inexact scenario-based probabilistic programming (ISPP) approach. The DIWM model can handle uncertainties presented as interval values and probability distributions, and it can support assessing the risk of violating system constraints. Several violation levels for facility-capacity and waste-diversion constraints are examined. Solutions associated with different risks of constraint violation were generated. The modeling results are valuable for supporting the planning of the study city's municipal solid waste (MSW) management practices, the long-term capacity expansion for waste management system, and the identification of desired policies regarding waste diversion. Sensitivity analyses are also undertaken to demonstrate that the violations of different constraints have varied effects on the planning of waste-flow allocation, facility expansion, and waste management cost.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Modeling Municipal Solid Waste Management System under Uncertainty

Huang

2010

Journal of the Air & Waste Management Association

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“…As a result, a large number of research efforts were undertaken to deal with the above difficulties in water resources management systems through various modeling approaches (Trezos and Yeh 1987;Chang et al 1996;Feiring et al 1998;Jairaj and Vedula 2000;Karamouz et al 2004;Li et al 2006a;Sethi et al 2006;Lu et al 2009;Sadegh et al 2010;Hu et al 2012;Amin et al 2013;Han et al 2013). Among them, two-stage stochastic programming (TSP) is effective in dealing with problems where an analysis of policy scenarios is desired and when the right-hand side coefficients are random with known probability distributions, and can facilitate the generation of effective management strategies (Maqsood and Huang 2003;Kara and Onut 2010;Noyan 2012).…”

Section: Introductionmentioning

confidence: 99%

An inexact optimization model associated with two robust programming approaches for water resources management

Liu

et al. 2014

Int. J. Environ. Sci. Technol.

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In this study, an interval fuzzy-robust two-stage stochastic-robust programming (IFRTSRP) model is developed for water resources management under uncertainty. The developed IFRTSRP model incorporates two-stage stochastic programming (TSP), fuzzy robust programming (FRP), interval linear programming (ILP), and stochastic robust optimization (SRO) within a general optimization framework. The IFRTSRP model can not only deal with uncertainties presented as probability distributions, fuzzy membership functions, discrete interval numbers, and their combinations, but also provide an effective linkage between the pre-regulated water resources management policies and the associated economic implications. The IFRTSRP model can also enhance the robustness for the optimization process by delimiting the uncertain decision space through dimensional enlargement of the original fuzzy constraints. Moreover, the IFRTSRP model can evaluate the trade-offs between system economy and stability by incorporating the variability measures on penalty costs into the objective function. The IFRTSRP model is applied to a hypothetical case study of water resources management. The results indicate that reasonable solutions would be generated under different levels of k and/or x (non-negative weight coefficients); moreover, a higher net system benefit would correspond to lower system stability and higher system failure risk. Thus, the modeling results can be used for generating decision alternatives and thus help the managers to identify desired water allocation policies based on the reasonable consideration of system economy, system stability, and system failure risk.

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“…Savic et al [11] described the advantages of genetic algorithms for the problem of least-cost design of water distribution networks. Li et al [12] developed an interval-parameter multi-stage stochastic linear programming method for water resources management under uncertainty. The mathematical programming model is suitable for conducting research on a multi-objective optimal water allocation and for water environmental management within a single administrative region [13,14].…”

Section: Introductionmentioning

confidence: 99%

Exploration of an Optimal Policy for Water Resources Management Including the Introduction of Advanced Sewage Treatment Technologies in Zaozhuang City, China

Yan

Sha

et al. 2016

Water

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Water shortage and water pollution are important factors restricting sustainable social and economic development. As a typical coal resource-exhausted city and a node city of the South-to-North Water Transfer East Route Project in China, Zaozhuang City's water resources management faces multiple constraints such as transformation of economic development, restriction of groundwater exploitation, and improvement of water environment. In this paper, we develop a linear optimization model by input-output analysis to study water resources management with the introduction of three advanced sewage treatment technologies for pollutant treatment and reclaimed water production. The simulation results showed that from 2014 to 2020, Zaozhuang City will realize an annual GDP growth rate of 7.1% with an annual chemical oxygen demand (COD) emissions reduction rate of 5.5%. The proportion of primary industry, secondary industry, and tertiary industry would be adjusted to 5.6%, 40.8%, and 53.6%, respectively. The amount of reclaimed water supply could be increased by 91% and groundwater supply could be decreased by 6%. Based on the simulation, this model proposes a scientific reference on water resources management policies, including water environment control, water supply plan, and financial subsidy, to realize the sustainable development of economy and water resources usage.

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An interval-parameter multi-stage stochastic programming model for water resources management under uncertainty

Cited by 245 publications

References 37 publications

Modeling Municipal Solid Waste Management System under Uncertainty

Modeling Municipal Solid Waste Management System under Uncertainty

An inexact optimization model associated with two robust programming approaches for water resources management

Exploration of an Optimal Policy for Water Resources Management Including the Introduction of Advanced Sewage Treatment Technologies in Zaozhuang City, China

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