The Performance Evaluation of Different Modular Construction Supply Chain Configurations Using Discrete Event Simulation
Bibliographic record
Abstract
Modular construction and off-site prefabrication methods are gradually replacing traditional on-site construction due to their many advantages, but a key problem remains how to derive the maximum amount of the benefit. Construction supply chain (CSC) management, which is able to create an intensive interflow and quality alignment among different sectors involved in the whole construction lifecycle, could potentially accelerate the development of modular construction. Nevertheless, the benefits of CSC management in terms of sustainability and economics may vary due to the various configurations of the supply chain to which CSC management is applied. Previous research focused mainly on the factors related to CSC management strategy and purchase decisions. Different modular construction supply chain configurations are rarely compared and have yet to be taken into consideration for impact analyzation. This paper aims to investigate the relationship between supply chain configuration and supply chain performance in the context of modular construction. Several supply chain configuration models based on a real modular construction project are proposed in this study. Discrete event simulation (DES) is used to test the impact of these supply chain configuration models on the overall supply chain performance. Evaluation criteria were developed for scenario comparison.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".