Date of Award
Spring 2008
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Engineering Management & Systems Engineering
Program/Concentration
Engineering Management and Systems Engineering
Committee Director
Patrick T. Hester
Committee Member
Resit Unal
Committee Member
Ariel Pinto
Call Number for Print
Special Collections LD4331.E555 K83 2008
Abstract
The goal of this study is to demonstrate the use of the Dempster-Shafer Theory of evidence as a decision aid to predict aircraft maintenance times during wartime operations using expert judgment elicitation. Increased precision in time estimation enables the jet engine aircraft maintenance facility commander to make more accurate decisions for the Air Force's wartime tactical operations allowing the commander to gain a decisive advantage. A questionnaire was designed to elicit judgments from experts in the Aircraft Maintenance Facility (AMF) to investigate maintenance times of the major failure modes (Ignition, Fuel, and Electrical). Results of the expert judgment elicitation were utilized through an application of the Dempster-Shafer Theory. This approach was presented as a plausible evidence combination technique when uncertain, incomplete, and potentially incorrect evidence needs to be assessed in a wartime environment. A decision aid that allowed comparison of each expert's assessment based on the Dempster-Shafer Theory was created, and an uncertainty assessment was discussed. An example showcasing two jet engine aircraft failures with both aleatory and epistemic uncertainty was presented to demonstrate use of the developed approach during wartime operations. The results of this methodology provide a useful and specific application area for the Dempster-Shafer Theory in the AMF to aid a decision maker in assessing the level of an expert's uncertainty.
Rights
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DOI
10.25777/kd3t-nb87
Recommended Citation
Kudak, Huseyin.
"Uncertainty Assessment of Aircraft Maintenance Times by Using Evidence Theory and Expert Judgment Elicitation"
(2008). Master of Science (MS), Thesis, Engineering Management & Systems Engineering, Old Dominion University, DOI: 10.25777/kd3t-nb87
https://digitalcommons.odu.edu/emse_etds/210
Included in
Aeronautical Vehicles Commons, Maintenance Technology Commons, Probability Commons, Statistical Theory Commons