Two-stage credibility-constrained programming with Hurwicz criterion (TCP-CH) for planning water resources management

2014 ◽  
Vol 35 ◽  
pp. 164-175 ◽  
Author(s):  
X.T. Zeng ◽  
Y.P. Li ◽  
W. Huang ◽  
X. Chen ◽  
A.M. Bao
Keyword(s):  
Water Resources ◽  
Water Resources Management ◽  
Two Stage ◽  
Resources Management ◽  
Constrained Programming ◽  
Hurwicz Criterion

scholarly journals Inexact Multistage Stochastic Chance Constrained Programming Model for Water Resources Management under Uncertainties

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Hong Zhang ◽  
Minghu Ha ◽  
Hongyu Zhao ◽  
Jianwei Song
Keyword(s):  
Water Resources ◽  
Stochastic Programming ◽  
Water Resources Management ◽  
Water Allocation ◽  
Programming Model ◽  
Management Systems ◽  
Chance Constrained Programming ◽  
Multistage Stochastic Programming ◽  
Resources Management ◽  
Constrained Programming

In order to formulate water allocation schemes under uncertainties in the water resources management systems, an inexact multistage stochastic chance constrained programming (IMSCCP) model is proposed. The model integrates stochastic chance constrained programming, multistage stochastic programming, and inexact stochastic programming within a general optimization framework to handle the uncertainties occurring in both constraints and objective. These uncertainties are expressed as probability distributions, interval with multiply distributed stochastic boundaries, dynamic features of the long-term water allocation plans, and so on. Compared with the existing inexact multistage stochastic programming, the IMSCCP can be used to assess more system risks and handle more complicated uncertainties in water resources management systems. The IMSCCP model is applied to a hypothetical case study of water resources management. In order to construct an approximate solution for the model, a hybrid algorithm, which incorporates stochastic simulation, back propagation neural network, and genetic algorithm, is proposed. The results show that the optimal value represents the maximal net system benefit achieved with a given confidence level under chance constraints, and the solutions provide optimal water allocation schemes to multiple users over a multiperiod planning horizon.

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