Integrated lean concepts and continuous/discrete-event simulation to examine productivity improvement in door assembly-line for residential buildings
Bibliographic record
Abstract
Efforts within the construction-manufacturing domain to improve assembly-line operations have benefited from paradigms emerging in the late-1990s such as lean manufacturing. This study investigates lean concept solutions to enhance productivity prior to capital investments. The investigation is carried on a case study of a single-door assembly line for residential buildings. Lean improvements are examined through an integrated continuous/discrete-event simulation approach, aiming to increase the productivity of the assembly line. The proposed simulation model incorporates factors representing the system reliability. Continuous simulation modelling, using state variable technique, is implemented to facilitate the observation of door unites in targeted sections to track the accumulation levels in these particular sections of the assembly line. In addition, proposed solutions for productivity improvements are implemented within the simulation model, such as introducing advanced alternatives for the automated stations and adding parallel stations. The effect on the productivity of the assembly line was successfully evaluated after the implantation of the proposed improvements in the simulation model. Relevant approaches can be implemented to evaluate and improve modular construction assembly lines prior to incurring capital investment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".