An Efficient Two-Stage Genetic Algorithm for Flexible Job-Shop Scheduling
Bibliographic record
Abstract
Flexible job shop Scheduling Problem (FJSP) is considered as an expansion of classical Job-shop Scheduling Problem (JSP) where operations have a set of eligible machines, unlike only a single machine at JSP. FJSP is classified as non-polynomial-hard (NP-hard) problem. Researchers developed different techniques including Genetic Algorithm (GA) that is widely used for solving FJSP. Regular GAs for FJSP determine both operation sequencing and machine assignment through genetic search. In this paper, we developed a highly efficient Two-Stage Genetic Algorithm (2SGA) that in the first stage, GA coding only determines the order of operations for assignment. But machines are assigned through an evaluation process that starts from the first operation in the chromosome and chooses machines with the shortest completion time considering current machine load and process time. At the end of the first stage, we have a high-quality solution population that will be fed to the second stage. The second stage follows the regular GA approach for FJSP and searches the entire solution space to explorer solutions that might have been excluded at the first stage because of its greedy approach. The efficiency of proposed 2SGA has been successfully tested using published benchmark problems and also generated examples of different sizes. The quality of the 2SGA solutions greatly exceeds regular GA, especially for larger size problems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".