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Operations Research, Systems Engineering and Industrial Engineering Commons™
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- Acquisition (1)
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- Confidence-intervals (1)
- Contract negotiation (1)
- Control bands (1)
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- Control charts (1)
- Control limits (1)
- Data signatures (1)
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- Infrared signatures (1)
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- Utility theory (1)
Articles 1 - 2 of 2
Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Foundations For A Game Theoretic Framework For Agile Acquisition, Scott Rosen, Kelly Horinek, Alexander Odeh, Les Servi, Andreas Tolk
Foundations For A Game Theoretic Framework For Agile Acquisition, Scott Rosen, Kelly Horinek, Alexander Odeh, Les Servi, Andreas Tolk
VMASC Publications
This article investigates the concept of developing a game theoretic framework that is based on the application of buyer and seller utility functions to support the bidding process in government acquisition. The results of a literature survey of utility function approaches, with potential to provide a suitable foundation to a game theory framework for acquisition, are presented. The utility function methods found most promising were further adapted and tested: the Best-Worst method, the Multi-Swing Method, and Functional Dependency for Network Analysis. To test the scalability of the approach, the Best-Worst method is applied to a larger problem to show the …
Detecting Special-Cause Variation 'Events' From Process Data Signatures, Timothy M. Young, Olga Khaliukova, Nicolas André, Alexander Petutschnigg, Timothy G. Rials, Chung-Hao Chen
Detecting Special-Cause Variation 'Events' From Process Data Signatures, Timothy M. Young, Olga Khaliukova, Nicolas André, Alexander Petutschnigg, Timothy G. Rials, Chung-Hao Chen
Electrical & Computer Engineering Faculty Publications
The ability to detect the special-cause variation of incoming feedstocks from advanced sensor technology is invaluable to manufacturers. Many on-line sensors produce data signatures that require further off-line statistical processing for interpretation by operational personnel. However, early detection of changes in variation in incoming feedstocks may be imperative to promote early-stage preventive measures. A method is proposed in this applied study for developing control bands to quantify the variation of data signatures in the context of statistical process control (SPC). Control bands based on pointwise prediction intervals constructed from the Bonferroni Inequality and Bayesian smoothing splines are developed. Applications using …