Model Choice in Multiobjective Decision Making in Water and Mineral Resource Systems
| dc.contributor.author | Gershon, Mark Elliot | |
| dc.date.accessioned | 2016-06-21T23:07:53Z | |
| dc.date.available | 2016-06-21T23:07:53Z | |
| dc.date.issued | 1981-05 | |
| dc.identifier.uri | http://hdl.handle.net/10150/614031 | |
| dc.description.abstract | The problem of model choice in multiobjective decision making, that is, the selection of the appropriate multiobjective solution technique to solve an arbitrary multiobjective decision problem, is considered. Classifications of the available techniques are discussed, leading to the development of a set of 27 model choice criteria and an algorithm for model choice. This algorithm divides the criteria into four groups, oily one of which must be reevaluated for each decision problem encountered. Through the evaluation of the available multiobjective techniques with respect to each of the model choice criteria, the model choice problem is modeled as a multiobjective decision problem. Compromise programming is then used to select the appropriate technique for implementation. Two case studies are presented to demonstrate the use of this algorithm. The first is a river basin planning problem where a predefined set of alternatives is to be ranked with respect to a set of criteria, some of which cannot be quantified. The second is a coal blending problem modeled as a mathematical programming problem with two linear objective functions and a set of linear constraints. An appropriate multiobjective solution technique is selected for each of these case studies. In addition, an approach for the solution of dynamic multiobjective problems, one area where solution techniques are not available, is presented. This approach, known as dynamic compromise programming, essentially transforms a multiobjective dynamic programming problem into a classical dynamic programming problem of higher dimension. A dynamic programming problem, modeled in terms of three objectives, is used to demonstrate an application of this technique. | |
| dc.description.sponsorship | I wish to thank Dr. Lucien Duckstein for his guidance in the development of the research described in this dissertation and for his assistance in directing my program of study. In addition, I am grateful to him for the opportunities presented to me while a graduate student to travel and to publish my research. I also wish to thank Drs. A. Wayne Wymore, Thomas Maddock, Dimitri Kececioglu, and Duane Dietrich for their careful evaluation of this dissertation and their helpful comments; Dr. Robert Bulfin for his advice and evaluation of my program of study, and Dr. Ferenc Szidarovszky of the Horticulture University, Budapest, Hungary, for helping me to formulate this research during my summer in Budapest. The research described herein was funded by the National Science Foundation Grants No. CME 7905010 entitled "Criteria for Choosing a Regression Method in Engineering Decision -Making," and INT 78 -12184 entitled "Decision- Making in Natural Resources Management." Finally, I would like to thank my family, especially my wife, Deborah, for supporting me in my decision to seek this degree and encouraging me along the way. As my research took form and grew so did my daughter, Sarah, and I am grateful for the flexibility of the work schedule which enabled me to be a part of both. | en |
| dc.language.iso | en_US | en |
| dc.publisher | Department of Hydrology and Water Resources, University of Arizona (Tucson, AZ) | en |
| dc.relation.ispartofseries | Technical Reports on Natural Resource Systems, No. 37 | en |
| dc.rights | Copyright © Arizona Board of Regents | en |
| dc.source | Provided by the Department of Hydrology and Water Resources. | en |
| dc.subject | Water resources development -- Mathematical models. | en |
| dc.subject | Mines and mineral resources -- Mathematical models. | en |
| dc.title | Model Choice in Multiobjective Decision Making in Water and Mineral Resource Systems | en_US |
| dc.type | text | en |
| dc.type | Technical Report | en |
| dc.contributor.department | Department of Hydrology & Water Resources, The University of Arizona | en |
| dc.description.collectioninformation | This title from the Hydrology & Water Resources Technical Reports collection is made available by the Department of Hydrology & Atmospheric Sciences and the University Libraries, University of Arizona. If you have questions about titles in this collection, please contact repository@u.library.arizona.edu. | en |
| refterms.dateFOA | 2018-06-13T05:54:16Z | |
| html.description.abstract | The problem of model choice in multiobjective decision making, that is, the selection of the appropriate multiobjective solution technique to solve an arbitrary multiobjective decision problem, is considered. Classifications of the available techniques are discussed, leading to the development of a set of 27 model choice criteria and an algorithm for model choice. This algorithm divides the criteria into four groups, oily one of which must be reevaluated for each decision problem encountered. Through the evaluation of the available multiobjective techniques with respect to each of the model choice criteria, the model choice problem is modeled as a multiobjective decision problem. Compromise programming is then used to select the appropriate technique for implementation. Two case studies are presented to demonstrate the use of this algorithm. The first is a river basin planning problem where a predefined set of alternatives is to be ranked with respect to a set of criteria, some of which cannot be quantified. The second is a coal blending problem modeled as a mathematical programming problem with two linear objective functions and a set of linear constraints. An appropriate multiobjective solution technique is selected for each of these case studies. In addition, an approach for the solution of dynamic multiobjective problems, one area where solution techniques are not available, is presented. This approach, known as dynamic compromise programming, essentially transforms a multiobjective dynamic programming problem into a classical dynamic programming problem of higher dimension. A dynamic programming problem, modeled in terms of three objectives, is used to demonstrate an application of this technique. |
