Dynamic Planning of Construction Activities Using Hybrid Simulation
Bibliographic record
Abstract
Traditional planning methods such as CPM and PERT have been useful tools to manage construction projects. However, the underlying model of these traditional methods often seems to fail to represent real projects as they tend to assume no interrelationship between project components. In reality, project components have complex dynamic feedback process that requires modeling of inherent uncertainty in the execution of these projects. Nevertheless, this dynamic nature and uncertainty have not been explicitly addressed by traditional planning methods. Project failure can be attributed to poor representation of the inner and outer aspects of operations that are responsible for project dynamics. Uncontrollable external forces are often cited but the real cause may be internal such as the feedback process among components of the project. An alternative perspective is offered in this paper through system dynamics (SD) that accounts for the feedback process and discrete event simulation (DES) for modeling the uncertainty. The proposed method utilizes SD method for modeling project dynamics and DES method coupled with CPM network for operational details and uncertainty, respectively. A case study that involves preparing engineering drawings in a design office is used to demonstrate the use of the developed method and to highlight its capabilities. Modeling the dynamic dimension is expected to enhance planning and scheduling of construction operations and to provide a better understanding of the impact of various internal and external factors on project schedule and productivity performance.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".