Learning-Based Approach to Integrated Operational Optimization Problems in Robot-Assisted Multistation Warehouse Systems
Bibliographic record
Abstract
In the era of booming e-commerce and Internet of Things technology, robotic mobile fulfillment systems (RMFSs) have gained more and more use in logistics industry. While these systems enhance labor efficiency, they introduce numerous optimization challenges. Order picking is a human–robot collaborative process in RMFS. It involves three critical and interrelated operational optimization issues: 1) PS; 2) resource scheduling; and 3) manual picking. Each of them is an NP-hard combinatorial optimization problem. Their integration is a significant challenge for operational optimization in RMFS with multiple picking stations and represents a novel problem that was not studied before to our best knowledge. To tackle this complex problem and fill the research gap, we first model it as a MIP to derive exact solutions for small-scale and illustrative cases. For industrial-scale cases that cannot be solved by exact methods given limited time, we propose a tailored learning-strategies-enhanced local search algorithm. It integrates a 3-D bi-section encoding strategy, a three-stage decoding policy, a learning-based ANS method, and two learning-based tabu mechanisms. Experimental results demonstrate the effectiveness of our proposed method, achieving 2.9% to 33.2% performance improvement over six competitive peers. This highlights its superiority in solving the concerned problem, providing significant potential for addressing practical order picking optimization challenges in RMFS.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".