Order picking dataset from a warehouse of a footwear manufacturing company
Bibliographic record
Abstract
This dataset originates from a real-world footwear manufacturing warehouse and provides a comprehensive foundation for benchmarking research in warehouse order-picking operations. Data was collected via SQL queries on the company's Warehouse Management System (WMS), resulting in diverse formats such as CSV files, CAD layouts, and Python scripts. The dataset includes geometric representations of the warehouse layout, with Cartesian-mapped storage locations, aisles, and central depots, detailed product classifications, storage positions, picking wave information, and routing paths. It supports evaluating various storage strategies, including Random, Class-Based, Dedicated, and Hybrid configurations, enabling the analysis of their impact on order-picking efficiency. Temporal data captures operational trends, including timestamps and operator-specific performance, offering insights into workflow efficiency and workload balancing. Anonymization and randomization techniques were applied while retaining realistic operational patterns to preserve confidentiality. This dataset is highly versatile and suitable for developing optimization algorithms for picker routing, order batching, wave generation, and intralogistics, as well as for advancing automation and robotics research through navigation-specific data for autonomous guided vehicles (AGVs) and robotic systems. This dataset significantly contributes to warehouse logistics research and operational optimization by supporting a wide range of applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".