Modeling Concurrent Product and Process Design Using a Game Theoretic Team Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract A satisfaction-driven game theoretic approach is developed with application to team-based concurrent product and process design (CPPD). This team approach for CPPD is based upon optimization formalism in which the design team is responsible to optimize overall product functionality (or performance) and the manufacture team pursues to minimize total manufacturing cost. This dual-team model characterizes the respective aspects of product design and process design. In particular, the preference of each team against a design configuration is characterized through the application of fuzzy set theory, whereby the method of Design for Satisfaction (DfS) can be applied to seek the most favorite design that best fulfills the team goal. Based on the strategic team paradigms derived from game theory, fuzzy set operators are used to aggregate satisfaction metrics of two teams. As a result, three team design models plus related algorithms are developed to reveal typical team interactions in the context of design computations. An illustrative example is worked out to demonstrate the satisfaction-driven team design models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it