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Research Papers

Approaches to Modeling the Gas-Turbine Maintenance Process

[+] Author and Article Information
Tai-Tuck Yu, James P. Scanlan, Richard M. Crowder, Gary B. Wills

e-mail: j.p.scanlan@soton.ac.uk School of Engineering Sciences, University of Southampton, Southampton SO17 1BJ, UKttyu@soton.ac.uke-mail: gbw@ecs.soton.ac.uk School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UKttyu@soton.ac.uk

Extend from Image That Inc., San Jose, CA.

J. Comput. Inf. Sci. Eng 12(1), 011007 (Dec 21, 2011) (9 pages) doi:10.1115/1.3647876 History: Received November 17, 2010; Revised July 01, 2011; Published December 21, 2011; Online December 21, 2011

Discrete-event modeling has long been used for logistics and scheduling problems, while multi-agent modeling closely matches human decision-making process. In this paper, a metric-based comparison between the traditional discrete-event and the emerging agent-based modeling approaches is reported. The case study involved the implementation of two functionally identical models based on a realistic, nontrivial, civil aircraft gas turbine global repair operation. The size, structural complexity, and coupling metrics from the two models were used to gauge the benefits and drawbacks of each modeling paradigm. The agent-based model was significantly better than the discrete-event model in terms of execution times, scalability, understandability, modifiability, and structural flexibility. In contrast, and importantly in an engineering context, the discrete-event model guaranteed predictable and repeatable results and was comparatively easy to test because of its single-threaded operation. However, neither modeling approach on its own possesses all these characteristics nor can each handle the wide range of resolutions and scales frequently encountered in problems exemplified by the case study scenario. It is recognized that agent-based modeling can emulate high-level human decision-making and communication closely while discrete-event modeling provides a good fit for low-level sequential processes such as those found in manufacturing and logistics.

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Copyright © 2012 by American Society of Mechanical Engineers
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Figures

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Figure 4

Variation of ABM and DEM execution times with size of simulated engine fleet or number of engine items

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Figure 5

Distribution of McCabe Cyclomatic Index at method and procedure level in the agent-based model and the discrete-event model, respectively

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Figure 1

A high-level taxonomy of modeling paradigms

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Figure 2

The Rolls-Royce Derwent cycle (diagram reproduced with permission of Rolls-Royce plc)

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Figure 3

The upper level of the discrete-event model showing lifecycle activities for an engine fleet from entry into airline service until final disposal

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