Robust Job Shop Scheduling with Condition-Based Maintenance and Random Breakdowns
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a simulation-optimization approach for solving the Job Shop Scheduling Problem (JSSP) under both planned and unplanned machine unavailability. Two maintenance policies are considered: Condition-Based Maintenance (CBM) and Corrective Maintenance (CM). The real makespan objective function is first approximated using several surrogate functions that are independently optimized using Genetic Algorithm (GA), before their best solutions are evaluated through simulation with stochastic degradation of machines, random breakdowns, and uncertain CBM and CM duration. To ensure schedule robustness, a weighted average of the expected makespan and its 90th percentile is used as the evaluation criterion, and the best schedule is added to an elite list to initiate the next iteration. A stopping rule inspired by Simulated Annealing (SA) is employed to prevent premature conversion. Numerical experimentation on random instances showed that the proposed approach can reach high-quality solutions effectively.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 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 it