Joint parallel-machine scheduling and maintenance planning optimisation with deterioration, unexpected breakdowns, and condition-based maintenance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The joint optimisation of production scheduling and maintenance planning can significantly decrease production interruptions (or stoppages) and, simultaneously, improve production stability and enhance the reliability and availability of equipment and machines. This paper studies the joint optimisation of production schedules and CBM plans in a parallel-machine production setting. The machines are subject to deterioration, unexpected breakdowns, and deterioration-based failures. The reliability of the machines is modelled as a multi-state system in which two deterioration thresholds are introduced to initiate maintenance and prevent deterioration-based failures. An integrated optimisation model is proposed to solve this new problem. The proposed model employs Markov chains to formulate machines' reliability and a matrix-based approach to estimate the expected processing times, energy consumption, and maintenance costs. Then, a mixed-integer programming model is proposed that jointly optimises production schedules and maintenance plans by minimising a weighted sum objective function that includes expected lateness, maintenance, and energy consumption costs. A genetic algorithm (GA) is used to solve the new problem, and extensive computational experiments are performed to test the performance of the proposed GA. The results show the superiority of the proposed GA for all the test problems compared to two well-known metaheuristics.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it