ECMS 2023 Proceedings edited by Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni
Bibliographic record
Abstract
Challenged by an unprecedented increase in product variety and demand variability, logistics systems are required to fulfill small customer orders at competitive costs and within short lead times, while keeping a high level of flexibility. In this context, companies are increasingly adopting flexible material handling solutions based on autonomous mobile robots (AMRs). This paper deals with AMR-based Automated Pick to Pallet Systems (APPSs), a novel solution for mixed-case palletizing that has never been studied in scientific literature. In these systems, palletizing robots pick boxes from single-item source pallets and place them on mixed pallets under construction. AMRs replenish the palletizing robots with source pallets and transport the mixed pallets to and from the different palletizers until completion. An agent-based simulation model for the estimation of AMR-based APPS performance is presented and validated. The developed model can be easily modified and adapted to test different layout configurations and management policies. Therefore, it provides support to companies evaluating the introduction of such systems and lays the grounds for further research on their suitability in different contexts, also in comparison with existing systems.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.193 | 0.110 |
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".