Part feeding scheduling for mixed-model assembly lines with autonomous mobile robots: benefits of using real-time data
Bibliographic record
Abstract
Mixed-model assembly is increasingly widespread to meet customer requirements for customisation and short delivery times. Flexible part feeding systems are required to timely replenish assembly stations with materials, avoid station idle times, and limit inventory levels on the shop floor. Part feeding scheduling is a complex and dynamic problem, affected by processing time fluctuations, equipment failures, and variations of product mix. Although real-time data of factory processes and resources is widely available and can be exploited using a digital twin of the part feeding system, there is a lack of scientific evidence on the benefits of using real-time data in part feeding scheduling. This research addresses this gap by developing an agent-based simulation model of a part feeding system with a fleet of autonomous mobile robots (AMRs) and comparing a real-time dynamic part feeding scheduling approach with static benchmark approaches. Numerical results indicate that using real-time data improves the performance of the part feeding system and the assembly system significantly, and allows improving the trade-off between the AMR fleet size and the total storage capacity on the shop floor, resulting in lower investment costs for AMRs given a certain storage capacity or lower required storage capacity given an AMR fleet.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".