A line balancing problem with parallel workers and cycle time minimization
Bibliographic record
Abstract
We study the problem of balancing assembly lines with parallel workers, motivated by features observed at a third-party logistics provider. The assembly line differs from the two known types of line balancing problems in the literature since it determines simultaneously the line cycle time and the number of workers per station. Furthermore, each station can be occupied by more than one worker and a restriction on the minimum number of stations is imposed. This new type of line balancing problem minimizes the line cycle time, where two types of decisions must be made: how to group the tasks into stations and how many workers to assign to each station. We adapt mathematical models based on assembly line balancing problems with parallel stations. Furthermore, we propose a new model based on network flow formulations for the special case of lines with a serial task structure, frequently observed at the company. We perform an extensive computational study with realistic instances in order to compare the speed of solving different formulations, which is important as such a problem is at the operational level. We also provide a sensitivity analysis of key parameters aiming to better understand the trade-offs and provide meaningful managerial insights.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".