Performance Evaluation of Genetic Algorithm and Particle Swarm Optimization in Off-Site Construction Scheduling
Bibliographic record
Abstract
Off-site construction (OSC) isgaining significant attention due to its promising benefits, including reduced time, cost, and waste, along with improved quality, productivity, and safety.However, the dynamic nature of the production process (i.e., nontypical process time) introduces challenges in OSC production line, such as: (i) bottlenecks: (ii) workstation idle time; and (iii) identification of an optimal production sequence.To leverage the full benefits of OSC, a superior production planning and scheduling optimization method become imperative.Therefore, this paper aims to compare the computational performance of the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for optimizing OSC production schedule.The methodology consists of the three key steps, including: (i) data analysis; (ii) development of GA and PSO algorithms; (iii) implementation of both GA and PSO in a real-life wall panel production line in Edmonton, Canada.The results reveal that GA outperforms PSO in minimizing project completion time (PCT).Specifically, for 160 wall panels, the PCT using GA is 6112 min, whereas with PSO, it is 6122 min.Conversely, PSO produces results more quickly than GA.For the same set of 160 wall panels, the model runtime is 17.97 sec for GA and 6.0 sec for PSO.The findings of this study offer valuable insights for production managers in selecting the most effective algorithm for optimizing production schedules.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.001 | 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".