Integrated scheduling of multi-objective lot-streaming hybrid flowshop with AGV under dynamic environments
Bibliographic record
Abstract
In modern intelligent manufacturing workshops, researchers increasingly integrate the transportation of Automated Guided Vehicles (AGVs) with production scheduling to enhance overall efficiency. However, in real-world production scenarios, such integrated scheduling systems are highly susceptible to stochastic disturbances stemming from unexpected equipment failures, thereby significantly undermining operational efficiency. This study focuses on the dynamic lot-streaming hybrid flowshop scheduling problem with automated guided vehicles (DLSHFSP–AGV) under a disruption-prone environment. A multi-objective mixed-integer linear programming model that accounts for machine and AGV failures is developed. Based on this model, an event-driven partial rescheduling strategy is proposed, in which the disrupted operations and delivery tasks are classified into three categories: retained, continued, and reconstructed. On the framework of NSGA2-MDDQN (NSGA-Ⅱ- Multi-objective double-depth Q learning algorithm) algorithm, which is the basis of existing research, the dynamic encoding mechanism and multi-stage decoding strategy are innovatively introduced to realize the collaborative optimization of the machine allocation, AGV scheduling, and process sequencing of the remaining tasks after the perturbation. Experimental results demonstrate that, compared to combined scheduling rules, NSGA-II, and DDQN algorithms, the proposed method achieves improvements of 18.59%, 41.05%, and 4.26% in makespan, machine idle time, and AGV travel distance, respectively. These enhancements significantly improve the robustness and optimization performance of the scheduling scheme under dynamic perturbations, offering a reliable dynamic scheduling solution for intelligent 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".