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Operations Research, Systems Engineering and Industrial Engineering Commons™
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Articles 1 - 6 of 6
Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Methodology For Simulation And Analysis Of Complex Adaptive Supply Network Structure And Dynamics Using Information Theory, Joshua V. Rodewald, John M. Colombi, Kyle F. Oyama, Alan W. Johnson
Methodology For Simulation And Analysis Of Complex Adaptive Supply Network Structure And Dynamics Using Information Theory, Joshua V. Rodewald, John M. Colombi, Kyle F. Oyama, Alan W. Johnson
Faculty Publications
Supply networks existing today in many industries can behave as complex adaptive systems making them more difficult to analyze and assess. Being able to fully understand both the complex static and dynamic structures of a complex adaptive supply network (CASN) are key to being able to make more informed management decisions and prioritize resources and production throughout the network. Previous efforts to model and analyze CASN have been impeded by the complex, dynamic nature of the systems. However, drawing from other complex adaptive systems sciences, information theory provides a model-free methodology removing many of those barriers, especially concerning complex network …
Attending To Scientific Practices Within Undergraduate Research Experiences, Gina Quan, Chandra Turpen, Andrew Elby
Attending To Scientific Practices Within Undergraduate Research Experiences, Gina Quan, Chandra Turpen, Andrew Elby
Faculty Publications
Ford (2015) argues for viewing “scientific practice” not as a list of particular skills, but rather, more holistically as “sets of regularities of behaviors and social interactions” among scientists. This conceptualization of scientific practices foregrounds how they meaningfully connect to one another and are purposefully employed in order to explain nature. We apply this framework in the context of undergraduate research experiences (UREs) to understand the early forms of student engagement in scientific practices, and how these specific forms of engagement may be consequential for students’ future participation. Using video from interviews with students and research mentors, we argue that …
The Impact Of Learning Curve Model Selection And Criteria For Cost Estimation Accuracy In The Dod, Candace Honious, Brandon Johnson, John J. Elshaw, A. B. Badiru
The Impact Of Learning Curve Model Selection And Criteria For Cost Estimation Accuracy In The Dod, Candace Honious, Brandon Johnson, John J. Elshaw, A. B. Badiru
Faculty Publications
The first part of this manuscript examines the impact of configuration changes to the learning curve when implemented during production. This research is a study on the impact to the learning curve slope when production is continuous but a configuration change occurs. Analysis discovered the learning curve slope after a configuration change is different from the stable learning curve slope pre-configuration change. The newly configured units were statistically different from previous units. This supports that the new configuration should be estimated with a new learning curve equation. The research also discovered the post-configuration slope is always steeper than the stable …
The Influence Of Operational Resources And Activities On Indirect Personnel Costs: A Multilevel Modeling Approach, Bradley C. Boehmke, Alan W. Johnson, Edward D. White, Jeffery D. Weir, Mark A. Gallagher
The Influence Of Operational Resources And Activities On Indirect Personnel Costs: A Multilevel Modeling Approach, Bradley C. Boehmke, Alan W. Johnson, Edward D. White, Jeffery D. Weir, Mark A. Gallagher
Faculty Publications
Indirect activities often represent an underemphasized, yet significant, contributing source of costs for organizations. In order to manage indirect costs, organizations must understand how these costs behave relative to changes in operational resources and activities. This is of particular interest to the Air Force and its sister services, because recent and projected reductions in defense spending are forcing reductions in their operational variables, and insufficient research exists to help them understand how this may influence indirect costs. Furthermore, although academic research on indirect costs has advanced the knowledge behind the modeling and behavior of indirect costs, significant gaps in the …
Short-Term Building Energy Model Recommendation System: A Meta-Learning Approach, Can Cui, Teresa Wu, Mengqi Hu, Jeffery D. Weir, Xiwang Li
Short-Term Building Energy Model Recommendation System: A Meta-Learning Approach, Can Cui, Teresa Wu, Mengqi Hu, Jeffery D. Weir, Xiwang Li
Faculty Publications
High-fidelity and computationally efficient energy forecasting models for building systems are needed to ensure optimal automatic operation, reduce energy consumption, and improve the building’s resilience capability to power disturbances. Various models have been developed to forecast building energy consumption. However, given buildings have different characteristics and operating conditions, model performance varies. Existing research has mainly taken a trial-and-error approach by developing multiple models and identifying the best performer for a specific building, or presumed one universal model form which is applied on different building cases. To the best of our knowledge, there does not exist a generalized system framework which …
A Recommendation System For Meta-Modeling: A Meta-Learning Based Approach, Can Cui, Mengqi Hu, Jeffery D. Weir, Teresa Wu
A Recommendation System For Meta-Modeling: A Meta-Learning Based Approach, Can Cui, Mengqi Hu, Jeffery D. Weir, Teresa Wu
Faculty Publications
Various meta-modeling techniques have been developed to replace computationally expensive simulation models. The performance of these meta-modeling techniques on different models is varied which makes existing model selection/recommendation approaches (e.g., trial-and-error, ensemble) problematic. To address these research gaps, we propose a general meta-modeling recommendation system using meta-learning which can automate the meta-modeling recommendation process by intelligently adapting the learning bias to problem characterizations. The proposed intelligent recommendation system includes four modules: (1) problem module, (2) meta-feature module which includes a comprehensive set of meta-features to characterize the geometrical properties of problems, (3) meta-learner module which compares the performance of instance-based …