Integrating dynamic pricing strategies and demand-driven supply planning in wood pellet supply chains: A stochastic optimization approach
Bibliographic record
Abstract
This study proposes a stochastic optimization model in wood pellet supply chains, integrating dynamic pricing strategies and demand-driven supply planning. In doing so, the correlation among supply, demand, pricing mechanisms, and profitability is analysed by implementing a Monte Carlo simulation approach with 10,000 iterations. Moreover, the seasonal demand fluctuations and production capacity constraints are considered in this model to provide a more realistic representation of market dynamics. To validate the model and show its applicability, the case study of a wood pellet supply chain in Quebec, Canada is considered. The results reveal a strong positive correlation between price and profit, indicating that approximately 71.6 % of the variance in profit can be explained by changes in price. The findings underscore the need for smart pricing strategies in the industry, as the model demonstrates the certainty in supply chain profitability across all simulated scenarios. Furthermore, the optimization model improves the profitability by leveraging economies of scale in transportation. The proposed approach serves as a robust decision-support tool for strategic planning in the wood pellet industry incorporating complex market dynamics.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".