Optimizing Rescheduling Intervals Through Using Multi-Armed Bandit Algorithms
Bibliographic record
Abstract
Well scheduling in oil and gas production in a virtual enterprise is a distributed and online scheduling problem. For such a scheduling problem, planned schedules are subject to unexpected disruptions or under- or over- estimated completion times. To reduce the impact of these uncertain events, schedule revision is necessary to keep the current schedule feasible and optimal in productivity. However, even though frequent schedule revisions may maximize the number of well tasks, it can also increase machine setup and transportation costs. This indicates the necessity of designing a systematic strategy for determining when to carry out schedule revisions. There is no trivial solution to this problem. In this research, we propose an approach to rescheduling interval determination through using a reinforcement learning - multiarmed bandit model. A set of experiments is conducted in a multiagent simulation environment. The results of the experiment demonstrate the effectiveness of the proposed approach in detecting optimal rescheduling intervals.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".