A simulation-based statistical method for planning modular construction manufacturing
Bibliographic record
Abstract
Modular construction is a promising alternative to conventional construction; offering improved productivity, quality, and safety. To realize these benefits, sequencing the module fabrication process in a manner that ensures efficient allocation of labor resources is essential. However, the varying sizes and design specifications of modules lead to high variation in process times at workstations, ineffective utilization of resources, and imbalanced production lines. To address these challenges, this paper proposes a simulation-based statistical method to plan the sequencing of module fabrication and the allocation of workers at workstations for such that productivity and control are improved. The method consists of four processes: (i) data collection to obtain historical and near real-time data; (ii) identification of significant impact factors affecting process times at workstations along the production line; (iii) development of a predictive model for forecasting process times at workstations using statistical analysis and probability distribution function; and (iv) planning the sequencing of module fabrication in a manner that ensures efficient labor allocation (i.e., crew size). The developed method is validated using data captured from a light gauge steel wall panel production line operated by a modular fabricator in Edmonton, Canada. The industrial partner produces both interior and exterior light gauge steel wall panels on a production line consisting of multiple workstations. First, five significant impact factors for each workstation among the design factors that highly influence the process times were identified in order to develop cycle time formula as a predictive model. The simulation model developed and implemented in conjunction with cycle time formula (CTF) in this case study was deemed to be a reliable predictive model (i.e. 89.39% accuracy), which can be used to improve productivity. The method is shown to be capable of assisting in decision-making by enabling production managers to better understand the effects of proposed changes to the production line prior to implementation. In this way, production managers can plan effectively and thereby reducing non-productive idle time.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".