A multi-objective, stochastic programming model in watershed management

Persistent Link:
http://hdl.handle.net/10150/191038
Title:
A multi-objective, stochastic programming model in watershed management
Author:
Goicoechea, Ambrose,1942-
Issue Date:
1977
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:
This research develops an interactive algorithm for solving a class of multi-objective decision problems. These problems are characterized by a set of objective functions to be satisfied subject to a set of nonlinear constraints with continuous policy variables and stochastic parameters. The existence of a decision situation is postulated in which there are N resources to be allocated so that P satisfactory objective levels may be attained. A probabilistic tradeoff development algorithm, labeled PROTRADE, is developed to provide a framework in which the decision maker can articulate his preferences, generate alternative solutions, develop tradeoffs among these, and eventually arrive at a satisfactory solution provided it exists. As the decision maker arrives at a vector-valued solution, with a value for each objective function, he also generates the probabilities of achieving such values. Then, as his preferences are articulated, he is able to trade-off objective function values against one another, and directly against their probabilities of achievement. A central assumption of this research is that there is not an "optimal" solution to the problem, but only "satisfactory" solutions. The reason for this is that the decision maker is allowed to have a dynamic preference structure that changes as the various tradeoffs are generated and new information is made available to him. The algorithm is developed in the context of parameters normally distributed. Several theorems are presented which extend the applicability of the algorithm to nonnormal random variables, specifically exponential, uniform, and beta random variables. A case study of the Black Mesa region in northern Arizona is provided to demonstrate the feasibility of the algorithm. This region is being strip-mined for coal and the managing agency must decide on the extent of several management practices. The practices or objective functions considered in the study are: (1) livestock production, (2) augmentation of water runoff, (3) farming of selected crops, (4) control of sedimentation rates, and (5) fish pond-harvesting. Finally, conclusions are presented and areas for future investigation are suggested.
Type:
Dissertation-Reproduction (electronic); text
Keywords:
Hydrology.; Watershed management -- Data processing.
Degree Name:
Ph. D.
Degree Level:
doctoral
Degree Program:
Systems and Industrial Engineering; Graduate College
Degree Grantor:
University of Arizona
Committee Chair:
Duckstein, Lucien

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleA multi-objective, stochastic programming model in watershed managementen_US
dc.creatorGoicoechea, Ambrose,1942-en_US
dc.contributor.authorGoicoechea, Ambrose,1942-en_US
dc.date.issued1977en_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.abstractThis research develops an interactive algorithm for solving a class of multi-objective decision problems. These problems are characterized by a set of objective functions to be satisfied subject to a set of nonlinear constraints with continuous policy variables and stochastic parameters. The existence of a decision situation is postulated in which there are N resources to be allocated so that P satisfactory objective levels may be attained. A probabilistic tradeoff development algorithm, labeled PROTRADE, is developed to provide a framework in which the decision maker can articulate his preferences, generate alternative solutions, develop tradeoffs among these, and eventually arrive at a satisfactory solution provided it exists. As the decision maker arrives at a vector-valued solution, with a value for each objective function, he also generates the probabilities of achieving such values. Then, as his preferences are articulated, he is able to trade-off objective function values against one another, and directly against their probabilities of achievement. A central assumption of this research is that there is not an "optimal" solution to the problem, but only "satisfactory" solutions. The reason for this is that the decision maker is allowed to have a dynamic preference structure that changes as the various tradeoffs are generated and new information is made available to him. The algorithm is developed in the context of parameters normally distributed. Several theorems are presented which extend the applicability of the algorithm to nonnormal random variables, specifically exponential, uniform, and beta random variables. A case study of the Black Mesa region in northern Arizona is provided to demonstrate the feasibility of the algorithm. This region is being strip-mined for coal and the managing agency must decide on the extent of several management practices. The practices or objective functions considered in the study are: (1) livestock production, (2) augmentation of water runoff, (3) farming of selected crops, (4) control of sedimentation rates, and (5) fish pond-harvesting. Finally, conclusions are presented and areas for future investigation are suggested.en_US
dc.description.notehydrology collectionen_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.typetexten_US
dc.subjectHydrology.en_US
dc.subjectWatershed management -- Data processing.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineSystems and Industrial Engineeringen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.chairDuckstein, Lucienen_US
dc.contributor.committeememberWymore, A. Wayneen_US
dc.contributor.committeememberBulfin, Robert L.en_US
dc.identifier.oclc212768013en_US
All Items in UA Campus Repository are protected by copyright, with all rights reserved, unless otherwise indicated.