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Related Experiment Video

Updated: Jun 5, 2025

A Rapid Method for Modeling a Variable Cycle Engine
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Automated generation of process simulation scenarios from declarative control-flow changes.

Daniel Barón-Espitia1, Marlon Dumas2, Oscar González-Rojas1

  • 1Systems and Computing Engineering Department, Universidad de los Andes, Bogotá, Colombia.

Peerj. Computer Science
|December 13, 2024
PubMed
Summary

This study introduces a new method for business process simulation, simplifying "what-if" scenario analysis. It uses generative deep learning to automatically create accurate simulation models with specified control-flow changes, overcoming complexity issues.

Keywords:
Control-flow changesData-driven simulationDeclarative specificationStochastic process modelWhat-if analysis

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Area of Science:

  • Business Process Management
  • Simulation Modeling
  • Artificial Intelligence

Background:

  • Business process simulation estimates the impact of changes on time and cost.
  • Data-driven simulation (DDS) discovers process models from event logs but can create complex models.
  • Model complexity hinders manual adjustments for "what-if" scenarios, especially control-flow changes.

Purpose of the Study:

  • To propose an approach for declarative specification and automated generation of "what-if" scenarios in business process simulation.
  • To address the complexity limitations of DDS methods in creating adjustable simulation models.

Main Methods:

  • Utilizes a generative deep learning model to produce event log traces reflecting user-specified control-flow changes.
  • Generates a stochastic process model from these traces.
  • Constructs a modified simulation model for "what-if" analysis based on the stochastic process model.

Main Results:

  • The proposed approach automates the generation of "what-if" simulation models with user-defined control-flow modifications.
  • Generated models maintain accuracy comparable to manually adjusted models.
  • Successfully overcomes the complexity barrier in DDS for scenario analysis.

Conclusions:

  • The approach enables efficient and accurate "what-if" analysis by simplifying the modification of complex DDS models.
  • Declarative control-flow specification combined with generative deep learning offers a powerful solution for business process simulation.
  • Facilitates easier exploration of process மாற்றங்கள் and their potential impacts.