Multi-objective optimization of natural gas supply chain in shortage period
Bibliographic record
Abstract
Natural gas consumption usually has the characteristics of seasonal fluctuations. Some countries have experienced gas shortages in recent years due to the imbalance between supply and demand, especially in winter or peak gas consumption periods. Under the conditions of a gas shortage, it is necessary to ensure the benefits of marketers and improve their satisfaction from users’ perspectives, so it is difficult to find the optimal purchase and sales strategy. Considering that different pipeline transportation pricing mechanisms and different gas distribution schemes have a great impact on the benefits of marketers, this paper proposes a multi-objective optimization model that maximizes the marketer’s benefits and minimizes the hypoxia index for different pipeline transportation mechanisms because of the supply-side gas shortage in the upstream gas source. The object-weighted method is used to process the multi-objective problem. The CPLEX solver is adopted to solve the optimization problem. Finally, the model is applied to a long-distance natural gas supply chain system to prove its applicability. After multi-objective optimization, under the two pipeline transport mechanisms, profits increased by 17.5% and 6.9%, respectively, and the hypoxia index decreased by 1.11 and 1.93, respectively, which greatly improved economic benefits and user satisfaction. Overall, the optimized scheme can maintain the stable operation of the natural gas supply chain and make up for the losses caused by the gas shortage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".