A multi-period risk sharing supply chain contract under consideration of price and demand uncertainties
Bibliographic record
Abstract
Exchange rate is extremely volatile and displays a Markovian regime switching property. This report proposes a multi-period procurement problem with a flexible quantity risk-sharing supply contract that may provide a prevention against exchange rate (FX) fluctuations for international traders. The buyer assumed to be encountered with a random price modelled by a regime-switching geometric Brownian motion and also random demand. The proposed risk sharing supply contract model helps to compensate supplier for the depreciating market price and also helps buyer when purchase price increases. According to the author’s knowledge, none of the studies in the literature considers a risk-sharing supply contract with random demand and random price while modelling the exchange rates by regime switching approach. Multi-period lattice model is developed for valuation of risk-sharing supply contract. The problem is solved with using dynamic programming approach. A numerical example and sensitivity analyses are presented to illustrate the proposed model.
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How this classification was reachedexpand
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".