Hybridizing Ant Colony Optimization by Beam Search for the Assembly Line Balancing Problem
Bibliographic record
Abstract
The type-I Assembly Line Balancing Problem (ALBP) focuses on the task assignment process with the objective of minimizing the number of workstations for a given cycle time.With the development of complex products, the problem size and the complexity in the assembly process is increasing.In this study, we hybridize the ant colony optimization algorithm via beam search (ACO-BS) in order to solve the type-I ALBP, and we focus more on the large scale ALBP in order to suit to the industrial requirements.We test ACO-BS with benchmark instances with a time limit of 360 seconds for one run, and the results show that 95.54% of the problems can reach their optimal solutions.In addition, since we want to explore the large scale ALBP, we generate 27 instances with a total of 400 tasks (the largest number of tasks in the benchmark instances of type-I ALBP is 297) randomly basing on the complexity indicators of order strength and processing time variation.There are three levels of order strength, 0.2, 0.6 and 0.9, and the time variation is set to be at 5-15, 65-75 and 135-145 levels.Meanwhile, the processing times of the tasks usually follow a unimodal or bimodal distribution, and we generate task times to follow three kinds of distribution respectively, unimodal distribution peaking at the bottom, unimodal distribution peaking in the middle and bimodal distribution.The comparison results with solutions obtained by the priority rule demonstrate the superiority of ACO-BS in solving large scale ALBP.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".