Genetic Algorithm-Driven Optimization of Scheduling and Preventive Measures in Parallel Machines
Bibliographic record
Abstract
This paper elucidates an algorithmic methodology aimed at optimizing the scheduling on parallel machines to minimize the total Makespan and bolster preventive protections.The scenario under consideration involves a predetermined set of 'c' jobs, processed on 'd' parallel machines, with the stipulation of non-preemptive task execution and a single job assignment per machine at any given time.The context predominantly revolves around production planning scenarios with fixed delivery dates.A mixed-integer programming approach is leveraged in the study to attain minimization of the Makespan, a key focus of this analysis.The problem at hand is tackled effectively through the application of both Genetic Algorithm (GA) and CPLEX algorithmic strategies.A thorough numerical analysis follows, aimed at evaluating the performance measures.The findings of this analysis promise to shed significant light on the optimization of scheduling and preventive measures in parallel machines, thereby paving the way for future research in this sphere.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".