A NEW ALGORITHM FOR U-SHAPED TWO-SIDED ASSEMBLY LINE BALANCING
Bibliographic record
Abstract
This study introduces a new hybrid design for a specific case of assembly lines, and proposes a multi-pass random assignment algorithm to find the minimum number of stations required. The algorithm also finds the sequence and the schedule of the tasks assigned. The new design is a combination of two-sided lines and U-shaped lines, which benefits from the advantages of both designs at the same time. One side of the line is arranged in U-shape allowing stations with crossovers, and the other side of the line is balanced like a traditional straight flow. Depending on product direction, either Left or Right side of the line can be designed in U-shape. Small and large-sized two-sided assembly line test-bed problems were solved using the algorithm. Optimal results are achieved for all small-sized problems. Due to the novelty of the design, results of largesized problems are compared to findings of studies on simple two-sided balancing. Algorithm produced better results in most of the cases.
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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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".