Scheduling of Changes in Complex Engineering Design Process via Genetic Algorithm and Elementary Effects Method
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
Engineering design changes constantly occur in a complex engineering design process. Designers have to put an appropriate procedure in place to handle these changes in order to realize successful product development in a timely and cost-effective manner. When many change propagation paths are present, selection of the best change evolution paths and distribution of change results to downstream tasks become critical to the progress management of the project. In this paper, based on the available change propagation simulation algorithm, a global sensitivity analysis method known as elementary effects (EE) is employed to rank the importance of each potential propagation path with those involved design dependencies in the process. Further, an EE-based heuristic design dependency encoding method is applied to the genetic algorithm which is then adopted to schedule the change updating process. Finally, the optimal results obtained by the complete search and the heuristic dependency encoding methods are compared to illustrate the improvements and effectiveness of the latter method.
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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 | 0.001 | 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 | 0.000 |
| 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