Heterogeneous products allocation in a warehouse – case study in the automobile industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the increasing complexity of contemporary supply chains and the diversity of products in warehouses, product allocation strategies are crucial for warehouse operational efficiency. This paper addresses the operational planning challenge related to product allocation in a warehouse with heterogeneous products and storage racks. While the literature offers solutions to theoretical problems, applications to practical cases are less suited for complex environments. The objective is to present a novel approach to the storage location assignment problem, designed for an environment involving heterogeneous products. Using real data and practical insights for product subclassification from a case study in the automobile industry, the proposed allocation method is compared with two other scenarios: an optimal allocation method and a random one. A heuristic approach is then developed. The results show a 54% increase in bin availability and a 29% reduction in total distance traveled compared to the random solution
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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 it