Multi-objective mixed-model assembly line balancing with hierarchical worker assignment: A case study of gear reducer manufacturing operations
Bibliographic record
Abstract
Assembly lines, generally speaking, can reduce production costs, shorten cycle times, and achieve higher quality levels. Since the current market is characterized by increasing product variability, mixed-model assembly lines, in which similar product models can be assembled simultaneously, are more suitable to respond to varied market demands than traditional single-model assembly lines. In addition, in an assembly line, tasks often differ in processing requirements, and workers may have different qualification levels. This study, therefore, aims to construct models for the multi-objective mixed-model assembly line balancing problem with hierarchical worker assignment (MO-MALBP-HW). The goal is to generate a suitable plan for a mixed-model assembly line balancing problem considering the constraint of a hierarchical workforce, the cost of a hierarchical workforce, and production cycle time. When the problem is simple, it can be solved by a mixed integer programming (MIP) model. When the problem becomes complex, it can be solved by a multi-objective genetic algorithm (MOGA) and a non-dominated sorting genetic algorithm II (NSGA-II) to obtain a near-optimal solution. The implementation of this model can effectively manage the multi-objective mixed-model assembly line balancing plan, thereby improving plant efficiency and reducing cost.
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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 | 0.000 |
| 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) | 0.004 | 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".