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Optimization Techniques and Calibration
Up to 6 hours
Genetic and OptQuest optimization Model calibration workflows Monte Carlo integration
Course Overview
This hands-on session covers the use of two powerful experiment types in AnyLogic — optimization and calibration — along with best practices for applying them effectively in real-world simulation projects.
Objectives
- Understand how optimization experiments work in AnyLogic
- Learn to configure and run optimization experiments
- Understand how calibration experiments work
- Apply calibration to align simulation models with observed data
- Incorporate Monte Carlo analysis into optimization and calibration workflows
Prerequisites
AnyLogic
- Interface familiarity, View Areas, Agents, Events, Functions, Databases
JAVA
- Variables (primitives), Parameters
Discrete Events
- Source, Seize, Delay, Release, Sink
Agent-Based Modeling
- Creating agents, reading agent characteristics from Excel, database query syntax
Statistical Foundations
- Uniform distribution, Monte Carlo simulations
Topics Covered
1. Optimization Experiments in AnyLogic
- How optimization works under the hood
- Genetic optimization vs. OptQuest optimization
- Walkthrough of experiment properties and configuration
- Defining constraints and requirements
- Interpreting the default UI optimization plot
2. Calibration Experiments in AnyLogic
- How calibration works
- Setting up calibration criteria and target metrics
3. Hands-On Exercise
- Apply a calibration experiment to an AnyLogic model
- Configure optimization parameters and interpret results
Acquired Knowledge
By the end of this session, participants will be able to:
- Configure and execute optimization experiments in AnyLogic
- Apply calibration experiments to align simulation output with real-world data
- Combine Monte Carlo analysis with optimization and calibration for robust decision support
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