Cold Storage Panels Delivery Route Optimizations
Bibliographic record
Abstract
MergiCold is an innovative subcontractor providing cold storage metal panels and installation services for temperature-controlled warehouses. Founded during the year 2022, the company is based in Atlanta, Georgia and operates by delivering insulated metal panels for hoisting partitions, ceilings, and flooring of cold storage warehouses at various project sites. A major development has been the expansion of materials delivered nationwide and internationally, to Mexico and Canada. As a result, MergiCold company would like to know if it is cost efficient and feasible to expand the size of their current staff pool and fleet. Currently, the company owns two trucks and has two full-time commercial truck drivers on staff. Meeting expectations of recent business expansion needs calls for a consideration to hire a third truck driver and purchase a third truck to make the additional deliveries. To help MergiCold in making a sound business decision, the senior design team, known as the “MergiCold Optimization Team” for Kennesaw State University’s-Industrial and Systems Engineering Senior Design class formulated a three pronged approach: (1) Applying combinatorial and integer linear programming optimization using the Traveling Salesman Problem and Vehicle Routing Problem; (2) Truck load capacity maximization using Cube-Master and (3) Comparative cost analysis.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".