Research on workload balance problem of mixed model assembly line under parallel task strategy
Bibliographic record
Abstract
Aiming at the inefficiency caused by an unbalanced workstation load in the mixed-model assembly line (MMAL), we study the assembly line (AL) design and load balancing problem under parallel tasks. Considering the task configuration cost, workstation opening cost and penalty cost of unbalanced load on the assembly line, a mixed integer programming model with the workstation’s space capacity constraint is established to formulate the mixed-model assembly line load balancing problem (MMALLBP), which is aiming at minimizing the total cost. In addition, the simulated annealing algorithm with an improvement strategy is proposed. Numerical experiments using the improved simulated annealing algorithm are superior to the solver in terms of solving time and stability, and the solving accuracy is higher than that of the traditional simulated annealing algorithm. Allowing parallel tasks can flexibly allocate tasks to the workstations, effectively use the idle time of the workstations, reduce the number of opened workstations, improve the production efficiency, reduce construction costs and the risk caused by the unbalanced load of AL.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".