An Integrated Approach to Line Balancing for a Robotic Production System with the Unlimited Availability of Human Resources
Bibliographic record
Abstract
More and more robots have been introduced into production systems. Such a system may be called robotic production system. One important feature with such systems is the improved versatility in terms of its capability of fulfilling different tasks. The study presented in this paper developed a general approach for balancing of a production line with consideration of robots along with their functions and locations, and reliability of the line by assuming the unlimited availability of human resources. The approach has two parts: (1) the concept design of the production line and (2) computational line balancing. Specifically, for (2), the line balancing problem is formulated into a multi-objective optimization model with three objectives (i.e., balance rate, smoothness index, and economic cost) and the decision variables including robots and their locations. A specific textile production system was taken as an example to illustrate how this approach works and to show its effectiveness. As a result, a considerable improvement of such a robotic production line has been achieved after optimization in terms of the Takt time and the balance rate, particularly the Takt time being reduced by 18.52% and the balance rate being increased by 51.84%. The proposed approach is general, thus applicable to other robotic production systems, and expandable to inclusion of human factors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".