Bibliographic record
Abstract
Product distribution represents a significant portion of logistics costs. A well designed distribution network may provide substantial long-term benefits, but as the infrastructure develops, the optimal strategy may not be taken for granted. Product distribution costs are dependent on the supply chain network design, and the issue assumes more importance in emerging economies. When the emerging economies mature with time, both the supply as well as demand points shift, thus making it necessary to re-visit the network design problem in the future. This case analyses the supply chain retail network design of a company that distributes petroleum products throughout the Sultanate of Oman. The network design strategy employs an optimization model to identify the depot locations and gas station allocations in its distribution network. The case leads to identify the petroleum depot locations and gas station allocations and allows for designing an efficient distribution system. Additionally, the case study provides an opportunity to explore the major challenges faced by the petroleum supply chains in emerging economies. These challenges include changed business scenarios due to the diversification agenda of these economies, memberships in trade organizations and bilateral agreements, emergence of additional competition, lowering or elimination of tariffs, less protectionism of the local companies, and other risks associated with the supply chains.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".