Research on storage location allocation in three-dimensional automated warehouse based on cargo damage control
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
In automated high-bay warehouses, the results of storage location allocation significantly impact the operational efficiency of subsequent warehouse operations. Considering that cargo loss within the warehouse is often caused by contact with equipment, this paper proposes an innovative dual-objective optimization model aimed at minimizing unit cargo loss and the average travel time of stacker cranes through rational storage allocation. The study’s findings indicate that different cargo sizes, shelf sizes, and operational modes have varying degrees of impact on stacker crane operational efficiency and cargo loss. A reasonable match between equipment and product sizes helps enterprises minimize space waste, expedite response to customer demands, and reduce operational costs. This study optimizes storage location allocation using the SPEA-II algorithm and performs a comprehensive comparison with the results from CPLEX and NSGA-II. The results demonstrate that the SPEA-II algorithm performs excellently across various problem scales, indicating that it is an effective method for solving storage location allocation issues.
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 | 0.001 | 0.001 |
| 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