A new mathematical model for the job shop scheduling problem with uncertain processing times
Bibliographic record
Abstract
Job shop scheduling (JSS) problem has been one of the most interesting research issues in the literature during the recent years. JSS problem has been studied in different forms of deterministic, fuzzy, and stochastic at different depths. The idea of robust optimization (ROP), on the other hand, has earned a particular value to become a popular subject of the breakthrough for problem solving affairs amongst the researchers. Based on the emerged opportunity for illustrating a new area of search, a robust JSS problem is proposed as a challenge to this boundary of knowledge. The proposed method is capable of handling the perturbation which exists amongst the processing times. In fact, in many real world job scheduling problems, a small change in the processing times, not only causes a non-optimal solution, but also the infeasibility of the final solution may also occur. The proposed robust method could guarantee that, a small deviation of the processing times does not affect the feasibility. The implementation of the proposed method is illustrated using some numerical examples and the outcomes of the investigation are discussed
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".