Optimal decision problem in a three-level closed-loop supply chain with risk-averse players under demand uncertainty
Bibliographic record
Abstract
In this paper, a stochastic model of a closed-loop supply chain (CLSC) with one risk-averse manufacturer, one risk-averse retailer and one risk-averse third party is developed.To analyze how the members make decisions about wholesale price, collection rate, retail price and sales effort under different decision-making structures, the optimal decision problem under uncertain price and sales effort-dependent demand is studied through development of four game theoretical models.The equilibrium results between various models are compared and the optimal decisions from each member's perspective are investigated.According to the results, the third party-led model has better performance than manufacturer-led model.The cooperation mode of manufacturer and retailer is beneficial for the whole chain and customers and the cooperation mode of manufacturer and third party is the most effective model to collect the used-product.Finally, to increase the performance of decentralized CLSC compared with the centralized CLSC, a coordination contract is developed.The results indicate that this contract is advantageous for the members of CLSC, the customers, and the environmental issues.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".