Application of genetic algorithm for scheduling and schedule coordination problems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The problems on scheduling and schedule co‐ordination usually have conflicting objectives related to user's cost and operator's cost. Users want to spend less time to wait, transfer and travel by public buses. Operators are interested in profit making by lesser vehicle operating cost and having a minimum number of buses. As far as level of service is concerned users are interested in lesser crowing while operators are concerned with maximizing profit and thus to have higher load factors. In schedule co‐ordination problems transfer time plays an important role. Users are interested in coordinating services with in acceptable waiting time whereas operators prefer to have lesser services and want to meet higher demands, which invariably increases waiting time. These problems have multiple conflicting objectives and constraints. It is difficult to determine optimum solution for such problems with the help of conventional approaches. It is found that Genetic Algorithm performs well for such multi objective problems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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