Time of Resource Allocation Decision Model Based on Multi-Objective Optimization in School Information Technology Promotion
Why this work is in the frame
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Bibliographic record
Abstract
Aiming at the allocation of teaching resources for school affairs scheduling, a decision-making model for school affairs scheduling is designed based on a multi-objective optimization model.The "conflict detection and repair" module is added after the "initial population generation" operation in the traditional genetic algorithm, which decouples the scheduling model and meets the needs of scheduling decision-making.The designed method is compared with the standard genetic algorithm and stochastic two-point crossover genetic algorithm on the data set, and then the efficiency of resource allocation for school scheduling is improved by solving an example problem.The average faculty satisfaction with scheduling is 2.8, which is about 17% higher than the second place NPGA.Applying the algorithms to a college scheduling project, the feasible solutions of the algorithms in this paper satisfy all the various constraints, and the results of the three-stage style algorithm in the selfselected course scheduling mode yield better solutions than the baseline algorithm based on the course set in any of the arithmetic cases.This paper provides an informative solution path for the allocation of school scheduling resources, which can satisfy the course allocation needs of the three parties: teachers, students and schools.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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