MONTREAL- CANADA Iterative product configuration with fuzzy logic*
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
Product configuration provides an important opportunity for taking advantage of a number of the benefits of mass customization. Mass customization is aimed at developing a wide external variety of products to satisfy individual customers, with managed internal diversity to prevent cost proliferation. In this context, we propose an iterative product configuration method applying fuzzy logic which is designed to improve product configuration by replacing features which are of less interest to the customer with features the customer prefers. Fuzzy preference relations are used to evaluate the various configurations through the iterative product configuration process. To measure the level of customer satisfaction for each configuration, a satisfaction rate is also proposed. The integration of fuzzy preference relations and an adapted pseudo-order preference model constitute the basis for the proposed configuration method. An illustrative example is provided to show the applicability and practicality of the method. Key words: product configuration, fuzzy preference, fuzzy ranking, fuzzy sets, mass customization. 1
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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 | 0.000 | 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