A multiple objective decision model for the evaluation of advanced manufacturing system technologies.

Persistent Link:
http://hdl.handle.net/10150/185552
Title:
A multiple objective decision model for the evaluation of advanced manufacturing system technologies.
Author:
Demmel, Johann George
Issue Date:
1991
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:
Ordinary financial measures oversimplify the evaluation of Advanced Manufacturing System Technologies (AMST). A multiple objective decision model is developed which avoids the shortcomings of the traditional evaluation methods. The model is comprised of three objectives: Pecuniary, Strategic, and Tactical. The Pecuniary objective is based upon traditional Discounted Cash Flow techniques, with the results normalized to a [-1, +1] (worst-best) scale. The Strategic and Tactical objectives are based upon the concept of qualitative flows, and a qualitative discounting method is employed to discount the qualitative costs/benefits to a present value. The three objectives are traded-off using the Composite Programming technique, resulting in a rank ordering of the alternatives which are under consideration. The three objectives of the model are broken down into attributes which define the objective, and these attributes are "mapped" into the organization of a manufacturing environment. It is shown that the model covers the entire manufacturing environment. It is shown that the model covers the entire manufacturing organization in accounting for the costs/benefits of the proposed AMST alternatives. The influence of the three objectives on the final score is analyzed using a mixture experiment. The mixture experiment provides insight into the effect of varying the importance of each objective and its effect on the final rankings. This provides the analyst a method to determine which attributes and/or objectives are critical for the AMST alternative being investigated. Realizing that the evaluation of AMST hinges on events which are to occur in the future, and these events are not known for certain, the procedure is extended to include a measure of risk. Cash flows, qualitative flows, interest rates, and project lengths are provided by the decision maker as pessimistic, most likely, and optimistic estimates. An analysis is provided where the inputs are assumed to be independent. The model is then further enhanced to allow time dependence between cash flows and qualitative flows of a single attribute. Results are provided as a mean and variance of the evaluation score, objective scores, and indices, along with frequency distributions. A case study analysis is provided which shows the application of the techniques developed in this work.
Type:
text; Dissertation-Reproduction (electronic)
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Systems and Industrial Engineering; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Askin, Ronald G.

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleA multiple objective decision model for the evaluation of advanced manufacturing system technologies.en_US
dc.creatorDemmel, Johann Georgeen_US
dc.contributor.authorDemmel, Johann Georgeen_US
dc.date.issued1991en_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.abstractOrdinary financial measures oversimplify the evaluation of Advanced Manufacturing System Technologies (AMST). A multiple objective decision model is developed which avoids the shortcomings of the traditional evaluation methods. The model is comprised of three objectives: Pecuniary, Strategic, and Tactical. The Pecuniary objective is based upon traditional Discounted Cash Flow techniques, with the results normalized to a [-1, +1] (worst-best) scale. The Strategic and Tactical objectives are based upon the concept of qualitative flows, and a qualitative discounting method is employed to discount the qualitative costs/benefits to a present value. The three objectives are traded-off using the Composite Programming technique, resulting in a rank ordering of the alternatives which are under consideration. The three objectives of the model are broken down into attributes which define the objective, and these attributes are "mapped" into the organization of a manufacturing environment. It is shown that the model covers the entire manufacturing environment. It is shown that the model covers the entire manufacturing organization in accounting for the costs/benefits of the proposed AMST alternatives. The influence of the three objectives on the final score is analyzed using a mixture experiment. The mixture experiment provides insight into the effect of varying the importance of each objective and its effect on the final rankings. This provides the analyst a method to determine which attributes and/or objectives are critical for the AMST alternative being investigated. Realizing that the evaluation of AMST hinges on events which are to occur in the future, and these events are not known for certain, the procedure is extended to include a measure of risk. Cash flows, qualitative flows, interest rates, and project lengths are provided by the decision maker as pessimistic, most likely, and optimistic estimates. An analysis is provided where the inputs are assumed to be independent. The model is then further enhanced to allow time dependence between cash flows and qualitative flows of a single attribute. Results are provided as a mean and variance of the evaluation score, objective scores, and indices, along with frequency distributions. A case study analysis is provided which shows the application of the techniques developed in this work.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)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.advisorAskin, Ronald G.en_US
dc.contributor.committeememberSanchez, Paul J.en_US
dc.contributor.committeememberThompson, William J.en_US
dc.identifier.proquest9200006en_US
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