Integrating Variance Reduction Techniques and Parallel Computing in Construction Simulation Optimization
Bibliographic record
Abstract
Efficient planning of construction operations is deemed necessary to meet project objectives. Researchers have used simulation optimization to select the optimum amount of equipment and number of crews for construction operations. However, the current state of the practice suffers from the long computation time and the presence of inferior solutions in the final Pareto front. The objective of this paper is to develop and evaluate a robust simulation optimization framework. This framework is capable of reducing the computation time, improving the quality of optimal solutions, and increasing the confidence level in the optimality of the optimal solutions. This paper proposes the integration of common random numbers and parallel computing to achieve the stated objective. The parallel computing is performed on a single multicore processor. Based on the case study, the proposed framework was able to reduce the computation time by 90.5%, achieve a speedup of 2, improve the hypervolume indicator by 3.44%, and increase the confidence level by at least 100%. The values of improvement achieved will not necessarily be the same when different hardware, simulation models, simulation software, and optimization algorithms are used. The proposed framework allows project planners to obtain superior optimal solutions faster, which will make the use of stochastic simulation optimization more appealing.
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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 | 0.001 |
| 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) | 0.001 | 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".