Integrated two-stage multi-factory assembly scheduling with maintenance considerations
Bibliographic record
Abstract
In the dynamic landscape of contemporary industry, integrating maintenance practices with production scheduling is essential for sustaining operational efficiency and competitiveness. This study addresses a two-stage multi-factory assembly scheduling problem, introducing an innovative approach that incorporates maintenance practices to enhance system reliability in the face of unexpected machine failures. For deterministic scheduling, a tailored mixed-integer programming model is presented to minimise the makespan. This model is extended to formulate a stochastic schedule, accommodating unforeseen machine breakdowns through stochastic distributions. The extension includes the integration of preventive and corrective maintenance activities into an integrated two-stage multi-factory assembly scheduling problem. To solve the resulting optimisation problem, a decomposition algorithm utilising an exact solver is proposed. This approach breaks down the main model into smaller models, addressing computational challenges. Comparative analyses against the widely adopted CPLEX software across various instances validate the effectiveness of our approach. A significant finding from this comparison is that our decomposition algorithm outperforms the exact solver, achieving the optimal solution at a faster rate. In sensitivity analyses, the results underscore the superior solution-finding capability of our integrated stochastic model compared to maintenance heuristics from existing literature.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".