Implementation of Linear Programming and Decision-Making Model for the Improvement of Warehouse Utilization
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
Warehouses are used to store raw materials, finished goods, defective products, tools, machinery, and other company assets until needed. In addition, the warehouse is a staging area for the storage and packaging of products delivered to the customer for consumer industries. Ideally, storage time, storage space, and delivery lead times are minimized by improving warehouse management. This study implements an integration of linear programming (LP) and decision-making models. The LP model provides decision-makers with the optimum quantity of products that can be stored in the warehouse based on different case scenarios considered in this study. Furthermore, the criteria affecting the space utilization of warehouses at total capacity are identified. An integrated approach of rough analytical hierarchical process (AHP) and rough technique for order preference by similarity to ideal solution (TOPSIS) is utilized to determine the best pallet placement on the respective rack. Additionally, this technique identifies the storage racks that require improvements in warehouse space utilization for the products. This methodological approach will help many industries and logistics teams make optimal decisions and improve productivity.
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 | 0.000 | 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 it