A Hybrid Framework for Modeling Construction Operations Using Discrete Event Simulation and System Dynamics
Bibliographic record
Abstract
Construction projects are characterized by their dynamic nature and operational details. This paper presents a hybrid simulation methodology; designed to model construction projects. The methodology utilizes Discrete Event Simulation (DES) and System Dynamics (SD). DES has been widely used in modeling construction operations; however, it lacks the ability to model the global aspects of operations being modeled and the cause-effect relations of simulation variables. SD is utilized to circumvent these limitations. Both simulation methods provide valuable decision support but none is individually capable of capturing the holistic nature of the operation being modeled. The developed methodology integrates DES and SD to utilize their respective advantages in simulating construction operations. The developed methodology encompasses five stages: 1) identification of model objectives, 2) decision criteria to assist in selecting simulation methodology, 3) building simulation model and identification of interface variables, 4) computation framework and 5) implementation and testing. The paper describes the essential features of the developed methodology and its computational framework and focuses primarily on the modeling aspects of SD. A case study project is analyzed to demonstrate the use of the developed methodology and to highlight its capabilities.
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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 | 0.000 |
| 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".