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dc.creatorDeines, Erich Vernonen_US
dc.date.accessioned2012-06-07T22:44:20Z
dc.date.available2012-06-07T22:44:20Z
dc.date.created1996en_US
dc.date.issued1996
dc.identifier.urihttp://hdl.handle.net/1969.1/ETD-TAMU-1996-THESIS-D454en_US
dc.descriptionDue to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to digital@library.tamu.edu, referencing the URI of the item.en_US
dc.descriptionIncludes bibliographical references.en_US
dc.descriptionIssued also on microfiche from Lange Micrographics.en_US
dc.description.abstractA goal directed simulation method using fuzzy cognitive mapping (FCM-GDS) is partially developed. The FCM-GDS system can be used for the analysis and experimental design associated with traditional manufacturing studies. The FCM-GDS system was constructed using the C++ programming language and the EXSYS expert system shell. All simulation models were constructed and analyzed using ARENA simulation software. Additional analyses were conducted using Excel spreadsheets. A goal directed system is an intelligent back-end to a simulation language that suggests specific changes to variables in the model according to a knowledge-based analysis of the previous output. Fuzzy cognitive mapping is a relatively new method of collecting and standardizing expert opinion about a problem domain. The information in a cognitive map is stored as a bidirectional graph of nodes (concepts) and arcs (causal connections). The causal connections in a fuzzy cognitive map are fuzzy values or qualifiers. The research was conducted in four steps. First, a set of important manufacturing concepts was identified and the directions of their causal interactions were developed. Next, a logical procedure for manipulating these causal weights in the context of an experimental design framework was formulated. Third, detailed experiments were run to ascertain estimates of the relevant causal weights in both a typical flow line environment and a typical job shop environment. Finally, the constructed FCM-GDS system was compared to a traditional response surface methodology in two similar experiments. The two simulation models tested were a lightly congested flowline and a highly congested job shop. The suggested goals dealt with throughput, cycle time, utilization, and cost. The allowable changes involved decreasing the arrival rate and adding additional machines. The specific results of this research were favorable. Over the limited tests performed, the FCM-GDS system decreased the number of simulation runs required to reach the neighborhood of a predetermined set of goals by 75% when compared to the response surface method. Additional tests are required to establish the procedure's usefulness over a wide variety of manufacturing situations.en_US
dc.format.mediumelectronicen_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoen_USen_US
dc.publisherTexas A&M Universityen_US
dc.rightsThis thesis was part of a retrospective digitization project authorized by the Texas A&M University Libraries in 2008. Copyright remains vested with the author(s). It is the user's responsibility to secure permission from the copyright holder(s) for re-use of the work beyond the provision of Fair Use.en_US
dc.subjectindustrial engineering.en_US
dc.subjectMajor industrial engineering.en_US
dc.titleA goal directed simulation method using fuzzy cognitive mappingen_US
dc.typeThesisen_US
thesis.degree.disciplineindustrial engineeringen_US
thesis.degree.nameM.S.en_US
thesis.degree.levelMastersen_US
dc.type.genrethesis
dc.type.materialtexten_US
dc.format.digitalOriginreformatted digitalen_US


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