Agent simulation and digital twins (IAM640)

Content

This course covers simulation modelling and digital twin technology at the asset level (entire production or service system) to enable and support more data-driven and model-based decisions. Throughout the course, we will explore various facets, including:

• Process Flow Modelling and Discrete Event Modelling.
• Industrial Asset Modelling and State Machine Modelling.
• Continuous Behaviour and System Dynamics Modelling.
• Asset Deterioration Modelling.
• Systemic Behaviour and Systems Archetypes.
• Agent-Based Modelling and Multi-Method simulation.
• Project Execution Model for Simulation Projects.
• Scenario Modelling and Design for Simulation Experiments.
• Verification and Validation Techniques.
• Spare Parts Supply, Inventory and Maintenance Logistics Modelling
• Life Cycle Costing and Lifetime Benefit Modelling.
• Model-based Systems Engineering and Digital Twin Building.
• Sustainability Modelling.

Learning outcome

Knowledge

• Understand the complexity of modern systems behaviours and multi-disciplinary systems e.g. energy systems.
• Gain an understanding of system thinking and its toolbox.
• Gain the required knowledge of modelling deterministic and stochastic processes, entities, events, conditions, and queues of several production and service systems.
• Gain the required knowledge of simulation analysis: discrete events, continuous dynamics, data collection, alternatives evaluation and comparison.
• Gain a comprehensive understanding of real-world production, service and energy systems, their behaviours and how to operate and maintain the greatest economic, social, and environmental benefits.

Skills

• Able to model discrete event processes, continuous processes and state-triggered processes
• Able to model multi-agent systems and processes with help of multi-method modelling
• Able to conceptualise any real-world production and service system and build a simulation model that mimics its behaviour.
• Able to collect the required data sets and select the effective distribution models to simulate the dynamic behaviour of the studied system.
• Able to simulate the system performance based on key metrics such as costs, throughput, cycle times, equipment utilization, energy losses and resource availability.
• Design simulation experiments and test the baseline behaviour of the simulated case and run "what-if" scenarios to evaluate proposed process changes.
• Build and visualize system performance dashboards, control panels, and 2D and 3D animations.
• Gain practical experience on the entire professional project exaction model for simulation projects, from systems analysis, project planning, conceptual modelling, computation modelling and until the project completion phase.

General competence

• Acquire system-thinking, problem-solving, multi criteria - decision-making approach.

Required prerequisite knowledge

Exam

Coursework requirements

Course teacher(s)

Course coordinator:

Course teacher:

Head of Department:

Method of work

Open for

Course assessment

Literature