An application of a genetic algorithm to retail staff scheduling
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
Staff scheduling has received increasing attention over the past few years because of its widespread use, economic significance and difficulty of solution. For most organizations, the ability to have the right staff on duty at the right time is a critically important factor when attempting to satisfy their customers' requirements. The purpose of this study is to develop a genetic algorithm (GA) for the retail staff scheduling problem, and investigate its effectiveness. The proposed GA is compared with the conventional, linear integer programming approach. The GA is tested on a set of six real-world problems. Three are tested using a range of population size and mutation rate parameters. Then all six are solved with the best of those parameters. The results are compared to those obtained with the branch-and-bound algorithm. It is shown that GA can produce near-optimal solutions for all of the problems, and for half of them, it is more successful than the branch-and-bound method.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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