Estimating optimal ABC zone sizes in manual warehouses
Bibliographic record
Abstract
The ABC storage is the most popular class-based policy for the storage location assignment in warehouses. It divides a storage area into three zones and assigns the most demanded products to the best-located zone. Despite the policy’s popularity, arbitrary zone sizes are commonly used, which can lead to major efficiency losses. We investigate how several factors, such as the warehouse layout, the demand characteristics, and the storage and routing policies, impact the solutions for the zone sizing problem. We propose a new methodology to solve it using machine learning models to predict the optimal zone sizes considering the mentioned factors. We simulate many common manual warehouse settings, such as the multi-block layout, demand distributions, and several operating policies, to observe which zone sizes lead to the best performance in each one. The data generated is used to train four regression models – ordinary least squares, regression tree, random forest, and multilayer perceptron – to predict the optimal zone sizes from the best ones observed. Computational experiments show that zone sizes provided by all models significantly improve the order picking efficiency when compared to the arbitrary zone sizes commonly used, notably for the one-zone (random policy), the two-zone (20/80 rule), and the three-zone (20/30/50) systems. The proposed methodology is easily adaptable for different warehousing systems and problems when enough data is available to train the models. The resulting linear functions and decision trees are made available and can be used by practitioners for determining zone sizes for their particular warehouse.
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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 | 0.007 |
| 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 | 0.000 |
| 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".