A decision-making framework for environmentally sustainable product design
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
Design is a process through which customer needs are transformed into product or service specifications, and then used to develop a model or prototype. The prototype is tested, and modifications are brought to it before the production process starts. Moreover, the design process may be divided into different stages, starting from the definition of the customer needs, going through the conceptual design phase and ending up with the detailed design. In this article, we address the conceptual design phase, where the customer needs are assumed to be known. The proposed approach considers, based on customer needs, primary and secondary design criteria. Each design criterion has a set of predetermined possible values (options) from which the designer may select. Making the best selection of all the design features while satisfying the customer needs in terms of cost, quality (customer preference) and environmental performance is a combinatorial problem and therefore a decision-making framework would be helpful for the designers. In this article, the design criteria are evaluated using fuzzy technique for order preference by similarities to ideal solution based on cost, quality and environmental sustainability. A multiobjective and a single-objective binary programming models are then developed and solved, and their optimal solutions are obtained. The multiobjective solutions provide the decision makers with the possible trade-offs, whereas the single-objective model solution can be used as a final decision-making tool. The proposed approach is implemented in a user-friendly software developed by the authors. A case study is conducted using a baby car seat for which three main and six secondary design criteria are considered. The obtained results show the effectiveness of the approach used.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
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