Logistic flows at Alfa Laval - An analysis of the possibilities for optimizing Alfa Laval's airborne freight flows.
Bibliographic record
Abstract
At Alfa Laval there are about 400 000 shipments every year that either go by sea, air, rail or road freight. To date there is no company strategy covering the entire transport domain although there are a lot of projects working on different parts of the problem.\n\nThe question is how to optimise the transportation system at Alfa Laval and to do that without increasing the transport cost for the company. The problem with delays and possibility for different transport modes will also be addressed.\n\nTo do this the author has first distinguish the geographical transportation flows to find an applicable area. Then find a suitable theory for optimising the rate between the transportation systems and in the end apply theories on the existing transportation pattern and recommend changes in the freight flows.\n\nThe geographical partitioning has resulted in a production site in Tumba, Sweden, and was selected as a representative. Furthermore, the analysis has been done on the freights sent by air, both standard and express, to the largest recipients in Asia.\n\nThe analysis shows that if there is a possibility to change between the different modes of transport an increase in the DOT, delivery-on-time, can be accomplished with 4,4% in average for the freights sent between the different areas.\n\n
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".