Simulation-Based Approach for Master Planning and Scheduling in Offsite Construction Supply Chain Management
Bibliographic record
Abstract
Effective coordination among supply chain entities, particularly factory production, transportation, and onsite assembly, is critical in offsite construction as a means of mitigating the risk of schedule delays. Offsite construction companies thus tend to develop master schedules that consider not only production capacities and resources in the factory but also delivery logistics and onsite assembly processes. However, the current scheduling practice still lacks integration of the various components of the supply chain, instead relying on a manual and time-consuming approach. As a result, the master schedules generated do not fully take into consideration the dynamic relationships among the respective operational schedules of each supply chain entity. This, in turn, leads to unstable supply chain performance and underutilization of resources and, ultimately, cost overruns and project delays. In this context, the present study proposes an automated master scheduling system that employs the hybrid simulation paradigm to develop an integrated supply chain model. The developed method is implemented in a case study of a residential prefabricated panel producer, with the results demonstrating the effectiveness of the developed system in expediting master schedule generation while boosting supply chain resource utilization.
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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 | 0.000 |
| 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".