An extended analytic network process method for optimal design of reconfigurable products considering dependency relations among descriptions of design/process candidates and evaluation criteria
Bibliographic record
Abstract
A reconfigurable product serves as multiple products to deliver different functions through reconfiguration processes to change between product configurations. An optimization method was developed in our previous research to identify both the optimal design and the optimal reconfiguration processes. Because the generic design or process considering different candidates was modeled by an AND–OR tree or graph, importance weights were assigned to nodes with an AND or OR relation, such that the less-important nodes were pruned to improve the optimization efficiency. In this research, an extended analytic network process method is introduced to further improve the quality of the optimal reconfigurable product design approach when dependency relations among descriptions of design/process candidates and evaluation criteria are considered. In this method, the initial weights of the design/process nodes in the AND–OR tree or graph are adjusted based on the dependency relations such that the weights which truly reflect their contributions to the solutions are achieved. In addition, multiple evaluation criteria similar to the evaluation measures used in optimization are selected to identify the weights of the design/process nodes. A case study has been implemented to demonstrate effectiveness of the extended analytic network process for improving the quality of optimal reconfigurable product design.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".