Makespan reduction using dynamic job sequencing combined with buffer optimization applying genetic algorithm in a manufacturing system
Bibliographic record
Abstract
The advancement in the computer technologies and its integration with the production system has become highly flexible to produce large family of products. One of the main objectives of flexibility is to reduce the setup cost and time to respond to the market demands. Even the most flexible system may invite some setup cost in job changes, it is often desirable to change the sequence of jobs to further reduction in the setup cost and its related time. In the present work, the influence of dynamic job sequencing on a diverging junction conveyor production line with the objective to save the production cost & time have been presented. A production line which produces different variety of jobs is considered, where the raw part of different jobs arrives from the source randomly. Each batch of job has to undergo different processing operations. A production line is modelled and simulated using various production elements and its influence on reducing the manufacturing time is presented. The object oriented, discrete event simulation software is used to model and simulate the production system. It has been observed that dynamic sequencing of the jobs reduces the processing time by an average of 17%.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".