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Record W2165464318 · doi:10.5555/2429759.2429830

Methodology for synchronizing discrete event simulation and system dynamics models

2012· article· en· W2165464318 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWinter Simulation Conference · 2012
Typearticle
Languageen
FieldDecision Sciences
TopicSimulation Techniques and Applications
Canadian institutionsConcordia University
Fundersnot available
KeywordsSynchronizingSynchronization (alternating current)Computer scienceDiscrete event simulationEvent (particle physics)Sequence (biology)System dynamicsReal-time computingSimulationDistributed computingArtificial intelligenceTransmission (telecommunications)

Abstract

fetched live from OpenAlex

Integrating Discrete Event Simulation (DES) and System Dynamics (SD) simulation methods require synchronization of their simulation clocks to ensure that actions are executed in an orderly manner. This paper presents a synchronization methodology for integrating DES and SD models. A hybrid simulation-based method consisting of SD components at the higher decision level and DES components at the lower decision level is expected to benefit from the developed method. The proposed methodology integrates DES and SD models on a single platform, which enhances the simulation of construction operations. It consists of three elements: (1) advancing mechanism, (2) DES advancing algorithm, and (3) messages sequence mechanism. The paper provides a description of the three elements of the synchronization method. An illustrative preliminary experiment that utilizes DES and SD engines is presented to demonstrate the use of the developed synchronization method and to illustrate its capabilities.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.934
Threshold uncertainty score0.627

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.424
GPT teacher head0.500
Teacher spread0.076 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it