Presentation of Multi-Skill Workforce Scheduling Model and Solving the Model Using Meta-Heuristic Algorithms
Bibliographic record
Abstract
<span lang="EN-US">In the present article, a multi-objective mathematical model for scheduling multi-skilled multi-objective workforce has been proposed with the aims of minimizing the number of night-shift engineers, minimizing the total cost of workforce and maximizing the number of engaged workforce. To solve the proposed model for scheduling workforce, bee colony optimization algorithm and DE algorithm have been employed, and in order to investigate the efficiency of these two algorithms, the results have been compared with each other in terms of quality, dispersion and uniformity factors. In order to solve the model three sample problems (40, 70 and 280 workforce) were designed and then solved by the two mentioned algorithms. Bee algorithm is able to find higher-quality answers. Also the results of the comparison of dispersion and uniformity index indicate that bee colony algorithm is able to find answers with more dispersion and more homogeneous than DE algorithm. The comparison of solution time of both algorithms indicate that bee colony algorithm is faster than DE algorithm and needs less time to reach quality, dispersed and homogenous answers.</span>
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".