Wood-Frame Wall Panel Sequencing Based on Discrete-Event Simulation and Particle Swarm Optimization
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Bibliographic record
Abstract
Wood-Frame Wall Panel Sequencing Based on Discrete-Event Simulation and Particle Swarm Optimization Mohammed Sadiq Altaf, Mohamed Al-Hussein, Haitao Yu Pages 254-261 (2014 Proceedings of the 31st ISARC, Sydney, Australia, ISBN 978-0-646-59711-9, ISSN 2413-5844) Abstract: In recent years off-site construction has become popular in North America due to the superior quality of the product, improved productivity, and reduced environmental impact. The panelized construction approach is one of the most readily utilized off-site construction methods. In a wood-frame panelized construction plant, wall panels are customized according to various design parameters such as length; height; number of studs, windows, and doors; panel type; and number of walls. These design parameters affect the processing time at each station in the plant, while the panel sequence affects the waiting time between stations. Due to this dynamic nature of the fabrication process, it is challenging to automatically generate an optimal panel sequence, as a result this task is performed manually in current practice. This paper focuses on integrating discrete-event simulation (DES) with an optimization algorithm in order to automate the panel sequencing process. Processing time at each station is calculated based on a task time formula which is a function of the design parameters of the panel, while delay is calculated based on a distribution derived from historical data. A particle swarm optimization (PSO) algorithm is integrated with the simulation model using a central database in order to generate an optimal panel sequence. The proposed method will eliminate the manual work required for panel sequencing, and is expected to reduce production time up to 10%. The proposed method is implemented in a wood-frame panelized construction plant as a case study. Keywords: Panelized construction, Discrete-event simulation, Particle swarm optimization, Panel sequencing DOI: https://doi.org/10.22260/ISARC2014/0034 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 it