Designing a resilient hydrogen hub under disruption risks and non-stationary demand distribution
Bibliographic record
Abstract
Hydrogen is a promising clean energy source that can reduce greenhouse gas emissions and enhance energy security. However, hydrogen supply chains face various uncertainties and challenges, such as demand fluctuations, sourcing disruptions, and environmental impacts. This paper proposes a novel two-stage stochastic model for hydrogen hub planning that addresses these issues. The first stage optimizes the order quantity from multiple suppliers by considering the actual inventory level and holding costs within the export period. The second stage streamlines the operations at the hub location by considering the resilience of suppliers, the utilization of alternative markets, the emissions from each source, and the terminal connection planning. To facilitate efficient model resolution, a Benders decomposition algorithm is proposed. The developed model and the robustness of the solution methodology were empirically tested and validated. The experimental design results affirm the model's capabilities under uncertainty and disruptions and, particularly, uncover substantial insights into the benefits of the proposed modeling approach. This paper contributes to the literature a robust framework that enhances hydrogen hub scheduling and sheds light on critical aspects of disruption management, supplier resilience, and emissions reduction in the hydrogen supply chain design.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".