Design Optimization for Sustainable Products Under Users' Preference Changes
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
Decisions made in the early design phase enormously contribute to the performance of a product during its life cycle. Since users' preferences may change over time, a product design should be revised under the preference change. Providing accurate data for designers ensures an optimal decision for product design; this research presents a new method to assess effects of the quantified changes on product cost and development time. In addition, two models to optimize design under unexpected disturbances are proposed. Normally, optimal parameters require several search iterations in design process before finalizing a product. The design time in terms of number of iterations can be reduced by adding resources in each iteration using modern control engineering methods. However, adding resources will increase the design cost. The proposed method in this research minimizes the total product design cost and environmental impacts under changes of users' preferences. The method is validated using an example of the smartphone design. The research novelty is a method of applying quantified changes of external disturbances (such as changes in users' preferences) in the design process, addressing a real problem in industry, and proposing optimal models of products for reduced cost and environmental impacts.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.010 |
| 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