Application of Reinforcement Learning with Recurrent Neural Networks for Optimal Scheduling of Flow-Shop Systems Under Uncertainty
Bibliographic record
Abstract
This study presents a methodology for the application of an intelligent agent for optimal scheduling of flow-shop manufacturing systems subject to uncertainty in processing times and demands. The agent is trained through a Deep Reinforcement Learning (DRL) algorithm referred to as Deep Recurrent Q-Learning (DRQN). The novelty of this work lies in the use of Recurrent Neural Network (RNN) as the structure of the agent, never considered before for scheduling of chemical manufacturing plants. This network aims to identify correlations between consecutive events (time-series) which are useful for the decision-making process of the agent for solving flow-shop scheduling problems. A reward function is set to guide the agent to a) minimize the makespan of the process inside a horizon, b) satisfy the demands without overproducing products, and c) account for uncertainty in processing times. The results show that this modelling framework can produce an agent that is able to re-schedule operations online due to realization of uncertainty and without the need to solve additional (online) optimization problems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".