Assembly line rebalancing and worker assignment considering ergonomic risks in an automotive parts manufacturing plant
Bibliographic record
Abstract
This paper recommends a new kind of assembly line rebalancing and worker assignment problem, taking ergonomic risks into account. Assembly line rebalancing problem (ALRBP) occurs when a current line must be rebalanced due to conditions such as changes in demand, production processes, product design, or quality issues. Although there are several research attempts on ALRBP in the relevant literature, only a few studies consider workers as unique individuals. This paper aims to solve the double reassignment problem: tasks to workers and workers to stations, considering ergonomic risk factors. This paper is the first study that comprises worker assignment and ergonomic constraints in ALRBP literature to the best of our knowledge. Objectives of our novel problem are to minimize rebalancing cost, which includes transportation of tasks and workers and minimize stations' ergonomic risk factors. A randomized constructive rule-based heuristic approach is developed to cope with the problem. The proposed solution approach is applied to benchmark data, and obtained results are promising. Moreover, the proposed solution approach is implemented in an automotive parts manufacturing plant.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".