An improved tabu search algorithm for the petrol-station replenishment problem with adjustable demands
Bibliographic record
Abstract
This paper discusses the petrol-station replenishment problem with adjustable demands (PSRP-AD). This problem originates from a practical application of fuel delivery, where the main objective is to satisfy all petrol-station demands at a minimal cost. However, the problem includes tanks with adjustable loads while respecting the agreement made between the company and the customers. This agreement allows the company to reduce the delivery up to a given threshold less than the ordered demand. For the PSRP-AD, we first describe the problem and provide the mathematical modelling that distinguishes between the loading and routing phases, followed by an improved tabu search to solve it. Within the framework of the tabu search, we embed the Kolmogorov–Smirnov statistic in the classical moves in order to speed up the search process. The results show that our solution is competitive on the benchmark instances and outperforms the current delivery method used by the company with a significant improvement.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".