A BIM-Based Simulation Model for Inventory Management in Panelized Construction
Bibliographic record
Abstract
Off-site construction is gaining more consideration from builders in North America, as it provides better quality products in less time and cost. Panelized construction is an increasingly popular off-site construction method in which panels are fabricated off site, then transported to the site for assembly. In this approach, panels are typically manufactured at a rate that exceeds that of on-site assembly of the panels, which necessitates inventory management of the fabricated panels awaiting transportation to the site for assembly. Effective inventory management is thus required in panelized construction to reduce costs. The randomness of the manufacturing and assembly process entails processing a large amount of information iteratively in order to select the proper production scenario to effectively manage the inventory. In this context, simulation provides an appropriate means of testing proposed scenarios in a timely manner. Simulation models require precise information, and building information modelling (BIM) provides a convenient and comprehensive means of data exchange among different environments. This paper thus presents a combination of discrete-event and continuous simulation that uses the information extracted from the BIM model to facilitate inventory management for panelized construction. This approach develops a schedule and ensures continuity and smoothness of the workflow.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".