Enhancing Warehouse Efficiency: A Study on Storage Allocation Assignment in a Footwear Manufacturing Company
Bibliographic record
Abstract
Efficient warehouse management relies heavily on aligning storage policies with operational strategies to minimise travel distances and improve order-picking efficiency. While storage location assignment policies provide structured frameworks for inventory organization, their impact on picking route optimisation based on real-world settings remains underexplored. This study reviews the main storage policies reported in the literature and evaluates the performance of four storage policies—Random, Class-Based, Dedicated, and Hybrid—within a dynamic warehouse environment of a shoe manufacturing company. The analysis identifies their effects on travel distances, variability, and overall efficiency by systematically comparing these policies. The results demonstrate that Dedicated and Hybrid policies consistently outperform others, with the Hybrid policy showing particular promise in environments with high product variability due to its balance between flexibility and structure. The findings offer practical insights for addressing the storage location assignment problem (SLAP) and highlight the value of integrating storage strategies to enhance warehouse operations. This research contributes to the literature by offering a comprehensive evaluation of storage policies and emphasising their role in addressing the operational challenges of modern warehouses.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".