Modeling Concurrent Product and Process Design Using a Game Theoretic Team Approach
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".