Modeling Evolution of Uncertainty in Sustainable Product Design
Bibliographic record
Abstract
Although a large number of research activities have been conducted for sustainable product development, it is not easy to find a practical method applied in sustainable product design as there are many uncertain factors existed in particular problems faced in different phases of product development. Based on reviewed literature, it is found that it is necessary to have an optimization metric accompanied with uncertainty effects in product development. A model is proposed in this research to evaluate the effects of uncertainty in product life cycle. The goal is to quantify various types of uncertainty from internal and external sources to assess the design efficiency for mitigating undesirable effects of uncertainty. The design phase is aimed in the research to look at product parameters that are subject to change in the design process. Inaccuracy, indecision and imprecision are selected as information uncertainty levels to quantify the evolution of uncertainty. Using the proposed concept, the discrete-event simulation (DES) is used to model and evaluate scenarios to minimize design phase duration of a sustainable wheelchair. Suggested improvements are compared to search the optimal solution. The analysis and comparison of scenarios show ways to reduce design time by (1) revision of the design process, (2) breaking down the product into design details, and (3) providing a clear and technical definition of uncertainty to be mitigated. The model is also used to locate areas for sustainability improvement in the studied case.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".