Integrated optimisation of production scheduling, maintenance, and quality control under non-homogeneous poisson process failures with multiple assignable causes
Bibliographic record
Abstract
Manufacturing optimisation often treats production scheduling, equipment maintenance, and statistical process control as independent entities, despite their inherently dependent nature. This paper proposes an integrated optimisation approach that simultaneously addresses these factors for enhanced decision-making. By considering non-homogeneous Poisson process failures and the influence of assignable causes, our framework evaluates possible scenarios within a production cycle and estimates expected cycle length and cost as well as the expected job completion time. The model optimises expected total costs, including those related to quality, maintenance, and penalties for late deliveries, while ensuring efficient production through equipment availability and constraint on maximum delay. We demonstrate the effectiveness of this approach through numerical studies, sensitivity analyses, and two real-world case studies: one in the food production industry and another in the automotive parts manufacturing industry based on the assumed data. In both cases, the model integrates production scheduling, maintenance, and quality control to minimise costs and ensure timely delivery. These case studies showcase this research's contribution to the development of more resilient and efficient manufacturing systems.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".