A game-theoretic framework for optimizing supply chain coordination and production
Why this work is in the frame
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Bibliographic record
Abstract
This research introduces a groundbreaking competition concept for supply chains, utilizing the Stackelberg game method to address internal entity interactions. In practical scenarios, chain components often partially cooperate, prioritizing individual benefits without a holistic understanding of the entire chain and market dynamics. Achieving complete chain coordination is challenging, expensive, and requires high-level agreement. Our study presents a simultaneous competition model for two supply chains and their internal entities, considering heterogeneous customers in price and time-sensitive classes. Each chain serves regular and special customers with varied delivery times and pricing. This research aims to investigate how competition among supply chains under various conditions impacts metrics like performance, market share and profits. These conditions include collaboration strategy (Centralized or Decentralized Structure) and production approach (Shared or Dedicated Capacity for specific customers). We employed scenario analysis with the Stackelberg Game framework to study strategic and policy choices' impact on supply chain conditions. We identified 10 distinct scenarios for analysis. Using the Stackelberg model, we iteratively solved the developed models until they reached equilibrium in price and delivery time. Our findings suggest that chains benefit more from a cooperative strategy with a Centralized Structure. Market behavior influences the chosen production approach, where adopting a dedicated capacity policy can lead to increased market share and profits if the market leader does so. Alternative strategies result in competitive stances and reduced returns for both chains.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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