Weather rebate sharing contract for enhancing supply chain performance
Bibliographic record
Abstract
Weather risk in a supply chain has become a potential issue. We propose a new class of contract, a weather rebate sharing contract, that enhances the supply chain performance under weather risk. To design the contract parameters, the risk aversion of supply chain members is taken into account. The Conditional Value at Risk (CVaR) function is used to measure a firm’s objective under risk. The optimal hedging and ordering decisions are obtained by maximizing the CVaR of the supply chain’s expected profit. The weather rebate sharing contract parameters have been designed so that both parties obtain a Pareto-improving solution. We have investigated the performance of the traditional revenue sharing contract and our designed contract under weather risk by using industry demand data and corresponding temperature data. Irrespective of the risk aversion of the supply chain members, we conclude that our designed contract coordinates effectively under all weather conditions and performs better than a traditional revenue sharing contract. Moreover, our results show that the designed contract coordinates under different weather conditions and provides a better coordination framework than the previously designed contracts under weather risk. We recommend that practitioners use this class of contract for administering the supply chain under weather-related uncertainties.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".