Inexact multistage stochastic integer programming for water resources management under uncertainty

In this study, an inexact multistage stochastic integer programming (IMSIP) method is developed for water resources management under uncertainty. This method incorporates techniques of inexact optimization and multistage stochastic programming within an integer programming framework. It can deal wit...

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Published in:	Journal of environmental management Vol. 88; no. 1; pp. 93 - 107
Main Authors:	Li, Y.P., Huang, G.H., Nie, S.L., Liu, L.
Format:	Journal Article
Language:	English
Published:	Oxford Elsevier Ltd 01.07.2008 Elsevier Academic Press Ltd
Subjects:	Agriculture Animal, plant and microbial ecology Applied ecology Biological and medical sciences Case studies Cities Conservation, protection and management of environment and wildlife Decision making decision support systems economic analysis Economics economies of scale Environment Environmental management Flood control floods Fundamental and applied biological sciences. Psychology General aspects Industry inexact multistage stochastic integer programming method Inexact optimization Integer programming mathematical models Models, Theoretical Multistage Optimization Penalties policy analysis Resource management risk management Software Stochastic analysis Stochastic models Stochastic Processes Studies Transactions Uncertainty Water allocation Water diversion Water management water policy Water resources Water resources management Water shortages Water Supply Integer programming Inexact optimization Multistage Stochastic analysis Uncertainty Decision making Environment Water resources Water resource management Environmental management Optimization
ISSN:	0301-4797, 1095-8630
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Abstract	In this study, an inexact multistage stochastic integer programming (IMSIP) method is developed for water resources management under uncertainty. This method incorporates techniques of inexact optimization and multistage stochastic programming within an integer programming framework. It can deal with uncertainties expressed as both probabilities and discrete intervals, and reflect the dynamics in terms of decisions for water allocation through transactions at discrete points of a complete scenario set over a multistage context. Moreover, the IMSIP can facilitate analyses of the multiple policy scenarios that are associated with economic penalties when the promised targets are violated as well as the economies-of-scale in the costs for surplus water diversion. A case study is provided for demonstrating the applicability of the developed methodology. The results indicate that reasonable solutions have been generated for both binary and continuous variables. For all scenarios under consideration, corrective actions can be undertaken dynamically under various pre-regulated policies and can thus help minimize the penalties and costs. The IMSIP can help water resources managers to identify desired system designs against water shortage and for flood control with maximized economic benefit and minimized system-failure risk.
AbstractList	In this study, an inexact multistage stochastic integer programming (IMSIP) method is developed for water resources management under uncertainty. This method incorporates techniques of inexact optimization and multistage stochastic programming within an integer programming framework. It can deal with uncertainties expressed as both probabilities and discrete intervals, and reflect the dynamics in terms of decisions for water allocation through transactions at discrete points of a complete scenario set over a multistage context. Moreover, the IMSIP can facilitate analyses of the multiple policy scenarios that are associated with economic penalties when the promised targets are violated as well as the economies-of-scale in the costs for surplus water diversion. A case study is provided for demonstrating the applicability of the developed methodology. The results indicate that reasonable solutions have been generated for both binary and continuous variables. For all scenarios under consideration, corrective actions can be undertaken dynamically under various pre-regulated policies and can thus help minimize the penalties and costs. The IMSIP can help water resources managers to identify desired system designs against water shortage and for flood control with maximized economic benefit and minimized system-failure risk.In this study, an inexact multistage stochastic integer programming (IMSIP) method is developed for water resources management under uncertainty. This method incorporates techniques of inexact optimization and multistage stochastic programming within an integer programming framework. It can deal with uncertainties expressed as both probabilities and discrete intervals, and reflect the dynamics in terms of decisions for water allocation through transactions at discrete points of a complete scenario set over a multistage context. Moreover, the IMSIP can facilitate analyses of the multiple policy scenarios that are associated with economic penalties when the promised targets are violated as well as the economies-of-scale in the costs for surplus water diversion. A case study is provided for demonstrating the applicability of the developed methodology. The results indicate that reasonable solutions have been generated for both binary and continuous variables. For all scenarios under consideration, corrective actions can be undertaken dynamically under various pre-regulated policies and can thus help minimize the penalties and costs. The IMSIP can help water resources managers to identify desired system designs against water shortage and for flood control with maximized economic benefit and minimized system-failure risk. In this study, an inexact multistage stochastic integer programming (IMSIP) method is developed for water resources management under uncertainty. This method incorporates techniques of inexact optimization and multistage stochastic programming within an integer programming framework. It can deal with uncertainties expressed as both probabilities and discrete intervals, and reflect the dynamics in terms of decisions for water allocation through transactions at discrete points of a complete scenario set over a multistage context. Moreover, the IMSIP can facilitate analyses of the multiple policy scenarios that are associated with economic penalties when the promised targets are violated as well as the economies-of-scale in the costs for surplus water diversion. A case study is provided for demonstrating the applicability of the developed methodology. The results indicate that reasonable solutions have been generated for both binary and continuous variables. For all scenarios under consideration, corrective actions can be undertaken dynamically under various pre-regulated policies and can thus help minimize the penalties and costs. The IMSIP can help water resources managers to identify desired system designs against water shortage and for flood control with maximized economic benefit and minimized system-failure risk. All rights reserved, Elsevier In this study, an inexact multistage stochastic integer programming (IMSIP) method is developed for water resources management under uncertainty. This method incorporates techniques of inexact optimization and multistage stochastic programming within an integer programming framework. It can deal with uncertainties expressed as both probabilities and discrete intervals, and reflect the dynamics in terms of decisions for water allocation through transactions at discrete points of a complete scenario set over a multistage context. Moreover, the IMSIP can facilitate analyses of the multiple policy scenarios that are associated with economic penalties when the promised targets are violated as well as the economies-of-scale in the costs for surplus water diversion. A case study is provided for demonstrating the applicability of the developed methodology. The results indicate that reasonable solutions have been generated for both binary and continuous variables. For all scenarios under consideration, corrective actions can be undertaken dynamically under various pre-regulated policies and can thus help minimize the penalties and costs. The IMSIP can help water resources managers to identify desired system designs against water shortage and for flood control with maximized economic benefit and minimized system-failure risk. An inexact multistage stochastic integer programming (IMSIP) method for water resources management under uncertainty is developed. In the IMSIP, uncertainties expressed as probability distributions and discrete intervals are tackled through a multilayer scenario tree. The developed method can reflect the dynamic complexities in water resources management systems, through transactions at discrete points of a complete scenario set over a multistage context. The results indicate that reasonable solutions are generated for both binary and continuous variables. They will help generate desired policies for water resources management with maximized economic benefit and minimized system failure risk. The results suggest that this method is applicable for other problems that involve dynamic complexities within a multistage dynamic context as well as uncertainties in modeling parameters as presented in multiple formats. In this study, an inexact multistage stochastic integer programming (IMSIP) method is developed for water resources management under uncertainty. This method incorporates techniques of inexact optimization and multistage stochastic programming within an integer programming framework. It can deal with uncertainties expressed as both probabilities and discrete intervals, and reflect the dynamics in terms of decisions for water allocation through transactions at discrete points of a complete scenario set over a multistage context. Moreover, the IMSIP can facilitate analyses of the multiple policy scenarios that are associated with economic penalties when the promised targets are violated as well as the economies-of-scale in the costs for surplus water diversion. A case study is provided for demonstrating the applicability of the developed methodology. The results indicate that reasonable solutions have been generated for both binary and continuous variables. For all scenarios under consideration, corrective actions can be undertaken dynamically under various pre-regulated policies and can thus help minimize the penalties and costs. The IMSIP can help water resources managers to identify desired system designs against water shortage and for flood control with maximized economic benefit and minimized system-failure risk. [PUBLICATION ABSTRACT]
Author	Nie, S.L. Huang, G.H. Li, Y.P. Liu, L.
Author_xml	– sequence: 1 givenname: Y.P. surname: Li fullname: Li, Y.P. organization: Department of Civil and Resource Engineering, Dalhousie University, Halifax, Nova Scotia, Canada B3J 1Z1 – sequence: 2 givenname: G.H. surname: Huang fullname: Huang, G.H. email: gordon.huang@uregina.ca organization: Environmental Systems Engineering Program, Faculty of Engineering, University of Regina, Regina, Sask, Canada S4S 0A2 – sequence: 3 givenname: S.L. surname: Nie fullname: Nie, S.L. organization: School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China – sequence: 4 givenname: L. surname: Liu fullname: Liu, L. organization: Department of Civil and Resource Engineering, Dalhousie University, Halifax, Nova Scotia, Canada B3J 1Z1
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Keywords	Integer programming Inexact optimization Multistage Stochastic analysis Uncertainty Decision making Environment Water resources Water resource management Environmental management Optimization
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Snippet	In this study, an inexact multistage stochastic integer programming (IMSIP) method is developed for water resources management under uncertainty. This method... An inexact multistage stochastic integer programming (IMSIP) method for water resources management under uncertainty is developed. In the IMSIP, uncertainties...
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SubjectTerms	Agriculture Animal, plant and microbial ecology Applied ecology Biological and medical sciences Case studies Cities Conservation, protection and management of environment and wildlife Decision making decision support systems economic analysis Economics economies of scale Environment Environmental management Flood control floods Fundamental and applied biological sciences. Psychology General aspects Industry inexact multistage stochastic integer programming method Inexact optimization Integer programming mathematical models Models, Theoretical Multistage Optimization Penalties policy analysis Resource management risk management Software Stochastic analysis Stochastic models Stochastic Processes Studies Transactions Uncertainty Water allocation Water diversion Water management water policy Water resources Water resources management Water shortages Water Supply
Title	Inexact multistage stochastic integer programming for water resources management under uncertainty
URI	https://dx.doi.org/10.1016/j.jenvman.2007.01.056 https://www.ncbi.nlm.nih.gov/pubmed/17532113 https://www.proquest.com/docview/195619016 https://www.proquest.com/docview/14862164 https://www.proquest.com/docview/20867628 https://www.proquest.com/docview/36892024 https://www.proquest.com/docview/48165412 https://www.proquest.com/docview/69169089
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