Increasing supply chain resiliency through equilibrium pricing and stipulating transportation quota regulation
Bibliographic record
Abstract
Supply chain disruption can occur for a variety of reasons, including natural disasters or market dynamics for which resilient strategies should be designed. If the disruption is profound and has dire consequences for the economy, it calls for the regulator’s intervention to minimize the impact for the betterment of the society. This paper investigates the minimum quota regulation on transport amounts of a shipping company with limited capacity that transports a group of products with heterogeneous transportation and production costs and prices. An interesting example can be found in the North American rail transportation market, where rail capacity is used for a variety of products and commodities, such as oil and grains. Similarly, in Europe, the supply chain for grain produced in Ukraine is disrupted by the Ukraine war and the blockade of the maritime transport routes. This siege puts pressure on the rail transport capacity of Ukraine and its neighboring countries to the west, which needs to be shared to ship a variety of products, including grains, military, and humanitarian supplies. Such situations require the proper execution of government intervention for effective management of limited transport capacity to avoid rippling effects throughout the economy. We propose mathematical models and solutions for market players and the government in a Canadian case study. Subsequently, the conditions that justify government intervention are identified, and an algorithm is presented to obtain the optimum minimum quotas.
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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 | 0.004 | 0.014 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".