A Game Theoretical Approach to Competition Between Members of a Green Supply Chain with Hybrid Products
Bibliographic record
Abstract
With progressing technologies green products are in competition with non-green products. In real world, there exist many firms that produce green and non-green products. In this study, a green supply chain(GSC) with a supplier and a manufacturer is considered. The supply chain(SC) produces green and non-green products. Market demands for both non-green and green products are available. These products might be replaced with each other. If the demand isn't satisfied by green products, hybrid production mode involving green and non-green products is chosen by the manufacturer. The government is considered as the leader of the game and fixes special tariffs(tax and subsid) for all products to control the market demand. A game theoretical model is formulated in four scenarios by considering collaboration of members in the SC. The ideal prices of raw materials, selling prices, and demand for green and non-green products are calculated. A numerical example that consists of sensitivity analysis of some main parameters is presented to compare the outcomes of various scenarios. The results indicate that collaboration between supplier and manufacturer has significant impact on a profit of GSC. Besides, Various consumer’s priorities are covered in hybrid production mode. The results show that by choosing hybrid production mode, the profit of SC decreases and hybrid production mode hasn't positive role on profit of SC and membership. Sensitive analysis shows that increasing tariffs by government causes an increase in summation profit function of GSC and profit function of member of GSC. Moreover, the demand for non-green products increases.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".