Fixed Charge Solid Transportation Problem Based on Carbon Emission with Budget Constraints in Uncertain Environment (UFSTPCEBC)
Bibliographic record
Abstract
The major factor affecting the limits of air pollution and climate change is the release of CO2 gas and other greenhouse gases as a result of several transportation systems.Moving forward, reducing carbon emissions should be our fundamental mission for a pollution-free environment.Once more, a single objective transportation system is rarely appropriate in cases that include multiple criteria.Therefore, for developing realworld transportation problems, multiple objectives are considered.There are some reservations or suspicions due to time constraints, data limitations, lack of information, or measurement flaws in real-world issues.Based on this fact, the decision-maker takes into account the designed problems' indeterminacy.Uncertainty theory has become a crucial tool for simulating real-world decision-making issues to handle this uncertainty.By creating an uncertain multi objective fixed charge solid transportation problem with carbon emission and budget constraints at each destination, this paper proposes a profit maximization, deterioration and time minimization technique that takes the possibility of indeterminacy into account.Here, goods are acquired at various source locations for varying rates, and they are subsequently carried to various destinations utilizing a variety of vehicles.The items are sold to the customers at different selling prices.The suggested model assumes that the following variables are uncertain: unit transportation costs, fixed charges, transportation times, supply at origins, demands at destinations, conveyance capacities, rate of carbon emission, rate of deterioration, and budget at destinations.We created an expect-chance constraint model utilizing uncertain programming approaches to simulate the suggested model.The uncertainty theory framework is used to develop this model.Goal programming is used to formulate and solve the equivalent deterministic transformations of these models.Finally, a numerical example that demonstrates the model is provided.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".