A Branch-and-Price Method for a Liquefied Natural Gas Inventory Routing Problem
Bibliographic record
Abstract
We consider a maritime inventory routing problem in the liquefied natural gas (LNG) business, called the LNG inventory routing problem (LNG-IRP). Here, an actor is responsible for the routing of the fleet of special purpose ships, and the inventories both at the liquefaction plants and the regasification terminals. Compared to many other maritime inventory routing problems, the LNG-IRP includes some complicating aspects such as (1) a constant rate of the cargo evaporates each day and is used as fuel during transportation; (2) variable production and consumption of LNG, and (3) a variable number of tanks unloaded at the regasification terminals. The problem is solved by a branch-and-price method. In the column generation approach, the master problem handles the inventory management and the port capacity constraints, while the subproblems generate the ship route columns. Different accelerating strategies are implemented. The proposed method is tested on instances inspired from real-world problems faced by a major energy company.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".