Investigating the productivity in different assembly system configurations for a better inclusion of disabled workers
Bibliographic record
Abstract
One of the United Nations’ Sustainable Development Goals is focused on decent work and growth which aims to reduce and, finally, remove all barriers for people with a form of diversity like disability. In such a context, manufacturing and production systems should be adapted by adopting specific equipment to help workers with disability while executing jobs according to the type of disability they report. Jobs must be properly planned since disabled workers have physical or cognitive disabilities and specific rights to work. Further, aiming to guarantee a real inclusion of workers with disability production systems should be designed to include these workers in the same working environment as workers without disability. This paper focuses on assembly systems, and it aims to investigate how different designs could impact both the productivity and inclusion of disabled workers. Then, due to the higher variety of products belonging to the same family mixed model assembly systems are considered. For each assembly system design, the daily productivity is calculated by using a simulation approach. Finally, according to the obtained results we provide some considerations about the convenience of adopting parallel flows to guarantee higher inclusion without affecting too much the productivity of the assembly system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".