Identification of Sustainable Transportation Energy Management Strategies Using a Stochastic Fractional Programming Approach under Uncertainties
Bibliographic record
Abstract
Transportation sector has a critical impact on the energy system and sustainable development. In this study, a stochastic fractional transportation-energy system planning model (SFP-LR) is developed for identification of sustainable system management strategies under uncertainties. Based on a hybrid of stochastic programming and fractional programming techniques, the proposed method can systematically reflect multiple complexities in such a management system. It can not only optimize the transportation energy consumption represented as output/input ratios, but also handle imprecise uncertainties in terms of left-hand-side random variables for examining the reliability of satisfying the constraints. The SFP-LR model is applied to support regional transportation-energy planning for demonstrating effectiveness of the developed approach. The solutions obtained from the SFP-LR approach can provide optimal transportation system planning schemes for regional CO2 emission control towards sustainable management under multiple complexities. It is also indicated that transport structure and management measures have important impacts on transportation energy consumption.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".