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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Bibliographic Details
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
Online Access:Get full text
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Description
Summary: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.
Bibliography:http://dx.doi.org/10.1016/j.jenvman.2007.01.056
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ISSN:0301-4797
1095-8630
DOI:10.1016/j.jenvman.2007.01.056