Use of graph network and hybrid simulation to understand schedule resiliency and capacity under disruptions
Bibliographic record
Abstract
The industrialized sector benefits from highly efficient manufacturing processes that are structured to provide a streamlined production, but its simplicity and efficiency often has the downside of not being adaptable enough to resist any type of disruption in its structure, such as sudden changes in resource availability, leading to delays in production schedules. While prior studies have examined optimization and buffer strategies, limited attention has been paid to how structural dependencies among tasks and resources contribute to system fragility. A simulation-based analysis of a floor framing manufacturing line in offsite construction is developed to evaluate the performance and behavior under disruptive scenarios such as worker and equipment unavailability. The model replicates the behavior of multiple interdependent workstations and resource constraints to identify how disruptions propagate across the system. Graph network analysis is used to identify and understand logical relationships, dependencies and interdependencies of flow between materials, resources, and information, leading to the identification of critical work and resource connections. By analyzing resource utilization, throughput, and idle times, the study reveals how critical resources and tasks with small disturbances can cause significant system-wide repercussions. The insights support resilience-building strategies by identifying critical elements and addressing structural vulnerabilities through capacity planning tailored to the network’s structure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".