Designing a resilient hydrogen hub under disruption risks and non-stationary demand distribution
Bibliographic record
Abstract
Hydrogen is a viable and sustainable energy alternative. It offers a solution to mitigate greenhouse gases and fortify energy security. However, its supply chain faces many uncertainties and challenges, including demand fluctuations and sourcing disruptions. This paper introduces an innovative two-stage stochastic model to plan hydrogen hub procurement, storage, and sales. In the first stage, the model optimises the order quantities by considering real-time inventory levels. This forward-thinking strategy aims to improve operational efficiency and adaptability. In the second stage, the model refines the hub operations by incorporating supplier resilience, exploration of alternative markets, emission considerations from each source, and terminal connection planning. Integrating these elements contributes to a comprehensive framework for robust hydrogen hub scheduling. This paper adopts a Benders decomposition algorithm to address the mathematical complexity of the model. This approach is necessary to ensure a smooth and efficient computational process. Empirical testing and validation of the developed model, along with the robustness of the solution methodology, emphasise its effectiveness in handling uncertainties and disruptions. This paper contributes to the existing literature by shedding light on critical facets of disruption management, supplier resilience, and emissions reduction within the hydrogen supply chain design.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".