Design Optimization for Sustainable Products Under Users' Preference Changes
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".