Inventory Policies and Supply Chain Coordination under Logistics Route Disruption Risks
Bibliographic record
Abstract
Predictable logistics disruptions due to scheduled lockdowns for large-scale events such as the Olympic Games may not only reduce supply chain profits, but also increase carbon emissions. To help solve these problems, an emergency transit policy to be applied to the logistics path is an effective solution. However, optimal inventory control is needed. This paper proposes an optimization model to control ordering and inventory policies for decentralized and centralized supply chains. The model considers the logistics path damping coefficient, the logistics path acceleration coefficient, and the vehicle loading capacity ratio in emergency transit. Our major findings include the following. First, supply chain profits under centralization are confirmed to be higher than under decentralization. Second, a price discount mechanism can achieve supply chain coordination. Third, the manufacturers in a centralized supply chain are more inclined to choose a logistics path with a high acceleration coefficient in order to let their cargo arrive quickly and to reduce the impact of the lead time demand fluctuations. Finally, the implications of our research results for carbon emission reductions are discussed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".