A General Approach for Solving Assignment Problems Involving with Fuzzy Cost Coefficients
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Bibliographic record
Abstract
Assignment problem is one of the most-studied, well known and important problems in mathematical programming. In this paper two different type of assignment problems are discussed: conventional and fuzzy assignment problem. In conventional assignment problem, cost is always certain. This paper develops an approach to solve the fuzzy assignment problem where cost is not deterministic numbers but imprecise ones. Here, the elements of the cost matrix of the assignment problem are triangular fuzzy numbers. Its triangular shaped membership function is defined. The optimal solution of fuzzy assignment problem is obtained successfully by using this approach. Compared with the result of conventional assignment problem, the result obtained by our approach is more advantaged for decision-makers. Finally, to show the efficiency of the proposed approach, the problem is demonstrated by one numerical example.
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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 | 0.001 | 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