Modeling Architecture for Hybrid System Dynamics and Discrete Event Simulation
Bibliographic record
Abstract
Construction systems and projects comprise complex combinations of subsystems, processes, operations, and activities. Discrete Event Simulation (DES) has been used extensively for modeling construction systems, addressing system complexity, and analyzing system behavior. However, while DES is a powerful tool for capturing operations as they occur in reality, DES does have difficulty modeling context and its mutual effects on the operational components of a system. System Dynamics (SD), on the other hand, captures feedback loops that are derived from the context level of a system and that can anticipate system behavior; nevertheless, SD cannot effectively model the operational parts of a system. Hybrid SD and DES modeling provide a set of tools that use the capabilities, while improving upon the disadvantages, of these two approaches. Although initial efforts to develop hybrid SD-DES modeling dates back to the late 1990s, in the construction industry, there are relatively few studies in this area, and there is still no robust architecture for hybrid system developers. This paper addresses these issues by proposing a comprehensive hybrid simulation architecture based on the High Level Architecture (HLA) infrastructure, which can be used by hybrid simulation developers in the construction industry. A typical steel fabrication shop has been modeled based on the proposed architecture, and it has been compared with the ideally developed hybrid simulation architecture.
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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 | 0.002 |
| 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 | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".