Integrated Process Planning and Scheduling Using an Optimized Rule-Mining Approach for Smart Manufacturing
Bibliographic record
Abstract
Manufacturing industries are undergoing a significant transformation towards Smart Manufacturing (SM) to cater to the ever-evolving demands of customized products. A major obstacle in this transition is the integration of Computer-Aided Process Planning (CAPP) with Scheduling. This integration poses challenges because of conflicting objectives that must be balanced, resulting in the Integrated Process Planning and Scheduling problem. In response to these challenges, our research introduces a novel hybridized machine learning-optimization approach designed to assign and sequence setups in Dynamic Flexible Job Shop environments via dispatching rule mining, accounting for real-time disruptions such as machine breakdowns. This approach seeks to bridge the gap between CAPP and scheduling by treating setups as dispatching units, ultimately minimizing makespan and bolstering manufacturing flexibility. The problem is modeled as a Dynamic Flexible Job Shop problem, and it is tackled through a comprehensive methodology that combines mathematical programming, heuristic techniques, and the creation of a robust dataset for data mining, which captures attributes reflecting priority relationships among setups. Empirical results validate the effectiveness of our methodology, demonstrating that the mining model surpasses classical dispatching rules. Furthermore, our model exhibits robust generalization capabilities in the context of SM, paving the way for more efficient and adaptive production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".