Approximate method for solving two-stage stochastic programming and its application to the groundwater management

Persistent Link:
http://hdl.handle.net/10150/191234
Title:
Approximate method for solving two-stage stochastic programming and its application to the groundwater management
Author:
Wang, Maili.
Issue Date:
1999
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Stochastic two-stage programming, a main branch of stochastic programming, offers models and methods to find the optimal objective function and decision variables under uncertainty. This dissertation is concerned with developing an approximate procedure to solve the stochastic two-stage programming problem and applying it in relative field. Five methods used in evaluating the expected value of function for distribution problem are discussed and their basic characteristics and performances are compared to choose the most effective approach for use in a two-stage program. Then the stochastic two-stage programming solving method has been established with the combination of a genetic algorithm (GA) and point estimation (PE) procedure. This approach avoids the inherent limitations of other methods by using PE to estimate the expected value of recourse function and the GA to search optimal solution of the problem. To extend the advantage of GA the modified genetic algorithm (MGA) is built to improve the performance of GA. Finally, the whole procedure is used in several examples with different kinds of variable and linear or nonlinear style objective functions. A stochastic two-stage programming model for an aquifer management problem is set up with considering conductivity and local random recharge as the source of uncertainty in the system. The designed procedure includes the response matrix process that replaces the partial differential flow equation, Girinski potential process and a pre-setup process that makes the response matrix process application in general aquifer random field possible. Other chosen problems are solved with designed approach to illustrate the effects of uncertainty source in the stochastic programming model and compared with results with ones given in literatures.
Type:
Dissertation-Reproduction (electronic); text
Keywords:
Hydrology.; Groundwater -- Management -- Mathematical models.; Stochastic programming.
Degree Name:
Ph. D.
Degree Level:
doctoral
Degree Program:
Civil Engineering and Engineering Mechanics; Graduate College
Degree Grantor:
University of Arizona
Committee Chair:
Lansey, Kevin E.

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleApproximate method for solving two-stage stochastic programming and its application to the groundwater managementen_US
dc.creatorWang, Maili.en_US
dc.contributor.authorWang, Maili.en_US
dc.date.issued1999en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractStochastic two-stage programming, a main branch of stochastic programming, offers models and methods to find the optimal objective function and decision variables under uncertainty. This dissertation is concerned with developing an approximate procedure to solve the stochastic two-stage programming problem and applying it in relative field. Five methods used in evaluating the expected value of function for distribution problem are discussed and their basic characteristics and performances are compared to choose the most effective approach for use in a two-stage program. Then the stochastic two-stage programming solving method has been established with the combination of a genetic algorithm (GA) and point estimation (PE) procedure. This approach avoids the inherent limitations of other methods by using PE to estimate the expected value of recourse function and the GA to search optimal solution of the problem. To extend the advantage of GA the modified genetic algorithm (MGA) is built to improve the performance of GA. Finally, the whole procedure is used in several examples with different kinds of variable and linear or nonlinear style objective functions. A stochastic two-stage programming model for an aquifer management problem is set up with considering conductivity and local random recharge as the source of uncertainty in the system. The designed procedure includes the response matrix process that replaces the partial differential flow equation, Girinski potential process and a pre-setup process that makes the response matrix process application in general aquifer random field possible. Other chosen problems are solved with designed approach to illustrate the effects of uncertainty source in the stochastic programming model and compared with results with ones given in literatures.en_US
dc.description.notehydrology collectionen_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.typetexten_US
dc.subjectHydrology.en_US
dc.subjectGroundwater -- Management -- Mathematical models.en_US
dc.subjectStochastic programming.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineCivil Engineering and Engineering Mechanicsen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.chairLansey, Kevin E.en_US
dc.contributor.committeememberYakowitz, Dianaen_US
dc.contributor.committeememberContractor, Dishawen_US
dc.contributor.committeememberSzidarovszky, Ferencen_US
dc.identifier.oclc224586906en_US
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