An expert system framework for finding robust system designs in discrete event simulation using a Taguchi strategy.

Persistent Link:
http://hdl.handle.net/10150/184772
Title:
An expert system framework for finding robust system designs in discrete event simulation using a Taguchi strategy.
Author:
Wild, Rosemary H.
Issue Date:
1989
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:
Well designed experiments can dramatically improve the statistical accuracy of simulation output data and facilitate the statistical analysis. In practice, however, the design of experiments is the most neglected phase of a simulation study. What is needed is good, sound experimental design strategies that can be easily implemented by practitioners. In this dissertation, an experimental design strategy is proposed for finding robust system designs in discrete-event simulation, where robust system designs are system designs that are insensitive to uncontrollable system noise. A conceptual framework for automating the proposed experimental design strategy is constructed and a prototype system for testing the efficacy of the proposed strategy is developed. Concentration is on the domain area of manufacturing systems. The experimental design strategy proposed in this dissertation can be broken down into three phases. The first phase involves finding a range of feasible values for the system of design variables. This is accomplished by augmenting the simulation program with an expert system. The second phase involves constructing experimental design plans. The experimental design plans suggested are plans that allow robust system designs to be found. The third phase, the optimization phase, involves searching for a best system design, where a best system design is a system design which optimizes the performance measures of interest and is not sensitive to uncontrollable system noise. The strategy proposed is evaluated with two models of a jobshop.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Experimental design.; Business -- Computer simulation.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Business Administration; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Pignatiello, Joseph J.; Marsten, Roy

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleAn expert system framework for finding robust system designs in discrete event simulation using a Taguchi strategy.en_US
dc.creatorWild, Rosemary H.en_US
dc.contributor.authorWild, Rosemary H.en_US
dc.date.issued1989en_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.abstractWell designed experiments can dramatically improve the statistical accuracy of simulation output data and facilitate the statistical analysis. In practice, however, the design of experiments is the most neglected phase of a simulation study. What is needed is good, sound experimental design strategies that can be easily implemented by practitioners. In this dissertation, an experimental design strategy is proposed for finding robust system designs in discrete-event simulation, where robust system designs are system designs that are insensitive to uncontrollable system noise. A conceptual framework for automating the proposed experimental design strategy is constructed and a prototype system for testing the efficacy of the proposed strategy is developed. Concentration is on the domain area of manufacturing systems. The experimental design strategy proposed in this dissertation can be broken down into three phases. The first phase involves finding a range of feasible values for the system of design variables. This is accomplished by augmenting the simulation program with an expert system. The second phase involves constructing experimental design plans. The experimental design plans suggested are plans that allow robust system designs to be found. The third phase, the optimization phase, involves searching for a best system design, where a best system design is a system design which optimizes the performance measures of interest and is not sensitive to uncontrollable system noise. The strategy proposed is evaluated with two models of a jobshop.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectExperimental design.en_US
dc.subjectBusiness -- Computer simulation.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineBusiness Administrationen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorPignatiello, Joseph J.en_US
dc.contributor.advisorMarsten, Royen_US
dc.contributor.committeememberNunamaker, Jay F.en_US
dc.contributor.committeememberFerrell, William R.en_US
dc.contributor.committeememberVakharia, Asoo J.en_US
dc.identifier.proquest9000151en_US
dc.identifier.oclc702683600en_US
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