Increasing supply chain resiliency through equilibrium pricing and stipulating transportation quota regulation
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it