A game-theoretic framework for optimizing supply chain coordination and production
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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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".