Transportation of Materials Under Fuzzy Environment Using Expected Monetary Value Strategy
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Bibliographic record
Abstract
There are many circumstances in real-word situations where multiple objectives were taken into account and optimized simultaneously.Developing an acceptable solution for a multi-objective optimizing problem might be done in various ways.The multiobjective optimization problems include assignment problem, transport problem, travelling salesman problem and many more.To better tackle real-world scenarios, a fuzzy set theory-based multi-objective transportation problem (MOTP) is examined.An understandable and direct method is intended to find the fully fuzzy multi objective transportation problems where the parameters are triangular fuzzy numbers.By applying a new ranking method and a new type of arithmetic operations on the parametric representation of triangular fuzzy numbers, we have obtained a nondominated solution of the fully fuzzy multi objective transportation problems.The new ranking method preserves the fuzzy nature of the problem.The Expected Monetary Value (EMV) strategy is applied to deal with the multi-objective optimization circumstances and a numerical example is provided to illustrate the strategy.
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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 | 0.000 | 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 | 0.000 |
| 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 it