A Novel Risk Programming Model for Water Resources Management under Uncertainty

2010 ◽  
Author(s):  
Liguo Shao ◽  
Shengji Luan ◽  
Ling Xu
Keyword(s):  
Water Resources ◽  
Water Resources Management ◽  
Programming Model ◽  
Resources Management ◽  
Risk Programming

scholarly journals An Inventory-Theory-Based Inexact Multistage Stochastic Programming Model for Water Resources Management

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
M. Q. Suo ◽  
Y. P. Li ◽  
G. H. Huang ◽  
Y. R. Fan ◽  
Z. Li
Keyword(s):  
Water Resources ◽  
Stochastic Programming ◽  
Water Resources Management ◽  
Programming Model ◽  
Water Shortage ◽  
Multistage Stochastic Programming ◽  
Dynamic Features ◽  
Inventory Theory ◽  
Resources Management ◽  
Water Resources Systems

An inventory-theory-based inexact multistage stochastic programming (IB-IMSP) method is developed for planning water resources systems under uncertainty. The IB-IMSP is based on inexact multistage stochastic programming and inventory theory. The IB-IMSP cannot only effectively handle system uncertainties represented as probability density functions and discrete intervals but also efficiently reflect dynamic features of system conditions under different flow levels within a multistage context. Moreover, it can provide reasonable transferring schemes (i.e., the amount and batch of transferring as well as the corresponding transferring period) associated with various flow scenarios for solving water shortage problems. The applicability of the proposed IB-IMSP is demonstrated by a case study of planning water resources management. The solutions obtained are helpful for decision makers in not only identifying different transferring schemes when the promised water is not met, but also making decisions of water allocation associated with different economic objectives.

scholarly journals An inexact multi-stage interval-parameter partial information programming model for water resources management under uncertainties

2020 ◽  
Author(s):  
Hongguang Chen ◽  
Zhongjun Wang
Keyword(s):  
Water Resources ◽  
Water Resources Management ◽  
Partial Information ◽  
Programming Model ◽  
Urban Water ◽  
Interval Parameter ◽  
Planning Time ◽  
Resources Management ◽  
Multi Stage ◽  
Linear Partial Information

Abstract The urban water shortage crisis around the world is increasing. In this study, an inexact multi-stage interval-parameter partial information programming model (IMIPM) is proposed for urban water resources planning and management under uncertainties. Optimization techniques of two-stage stochastic programming (TSP), interval-parameter programming (IPP), linear partial information theory (LPI) and multistage stochastic programming (MSP) are combined into one general framework. IMIPM is used to tackle uncertainties like interval numbers, water inflow probabilities expressed as linear partial information, dynamic features in a long planning time and joint probabilities in water resources management. It is applied to Harbin where the manager needs to allocate water from multi-water sources to multi-water users during multi-planning time periods. Four water flow probability scenarios are obtained, which are associated with uncertainties of urban rainfall information. The results show that the dynamics features and uncertainties of system parameters (such as water allocation targets and shortage) are considered in this model by generating a set of representative scenarios within a multistage context. The results also imply that IMIPM can truly reflect the actual urban water resources management situation, and provide managers with decision-making space and technical support to promote the sustainable development of economics and the ecological environment in cities.

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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