<title>PLETS model: a sustainability-concept-based approach to product end-of-life management</title>
Bibliographic record
Abstract
The need for sustainable product end-of-life management technologies is critical in today's globally competitive environment. The ever-increasing environmental consciousness of consumers and strictness in legislative regulations necessitate more prudent product decisions. The ability to make sound decisions on which product end-of-life management technologies to adopt is crucial to achieving sustainability of the product systems. It is essential that effective assessments of these technologies for future investment and applications indicate the total economic, environmental and social impacts of each option as well as the trade-offs between the various product end-of-life management technologies. The tendency in modeling this decision scenario is to base the formulation and the analysis on crisp, deterministic, and precise data. The product end-of-life management decision environment is however characterized by a mix of crisp and linguistically expressed parameters, most of which are uncertain in nature. Furthermore, the decision makers are interested in selecting an option that both satisfies certain minimum requirements and maximize their utility from a set of feasible alternatives. The goal of this study therefore is to develop a simple, efficient procedure that provides the manufacturing and allied industry with the ability to assess and evaluate the sustainability of remanufacturing and related technologies based on lifecycle thinking. This methodology, termed "product lifecycle extension techniques selection (PLETS) model," is a hybrid of fuzzy logic and a number of multi-attribute decision making models. It can be used to determine the remanufacturability of each product. In addition, it can also be employed to compare the economic, environmental and social sustainability of the feasible set of the product end-of-life management technologies being considered. The proposed methodology is illustrated with an example of end-of-life management for a peanut-shelling machine.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".