Energy-aware flexible job shop scheduling problem and preventive maintenance under the limited resource constraints
Bibliographic record
Abstract
<p>This thesis presents the development of a novel model for solving the flexible job shop scheduling problem with maintenance activities where maintenance activities are limited by a maintenance crew constraint. Moreover, in order to extend it in terms of energy consumption, the cost of energy usage associated with different states of the machines is considered in the objective function. The objective is to minimize the total cost of hiring repairmen, energy consumption, and tardiness penalties. We assume the production machines in this environment may break down which causes the unavailability of the machines for the production. In the maintenance phase, a threshold-based maintenance strategy is applied based on the obtained optimal replacement age of each machine. Accordingly, the required maintenance action is divided into two categories: minimal repair or replacement activities. Furthermore, opportunistic maintenance is considered in the scheduling to minimize the required number of repairmen to be hired. In fact, the main aims are to find the optimal machine assignment and operation sequence, to determine if preventive maintenance is required to be executed between two consecutive operations, and to specify the optimal number of maintenance crew to be hired for the shop floor to minimize the expected total cost.</p>
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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 | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 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 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".