Agent simulation and digital twins (IAM640)

The simulation engineering or simulation modelling process involves making a simulation model (a digital prototype) of a system or process, which can be a process plant, production line, warehouse, transportation system, service centre or other systems which allows you to identify the expected performance and resolve potential issues, challenges and problems before they happen. Simulation has proven to be a valuable tool for identifying layouts and operating strategies with the greatest economic, social, and environmental benefits. Simulation modelling is becoming even more popular and important as it is the core component of many recent digital twins.

Course description for study year 2024-2025


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Semester tution start


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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


  • 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.


  • 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



Form of assessment Weight Duration Marks Aid
Folder 1/1 1 Semesters Letter grades All

The folder is assessed through four assignments: Concept assignment 20%, Lab assignment 20%, Course project assignment 30% and Reflection assignment 30%. All assignments are individualContinuation options are not offered. Students who do not pass can carry out a new assessment the next time the subject is taught.

Coursework requirements

Compulsory exercises
Laboratory exercises, company visits, and guest lectures.

Course teacher(s)

Course coordinator:

Idriss El-Thalji

Course teacher:

Idriss El-Thalji

Head of Department:

Mona Wetrhus Minde

Method of work

Lectures, assignments, laboratory exercises, company visits, guest lectures.

Overlapping courses

Course Reduction (SP)
Modeling and simulation for sustainable operation 4.0 (OFF640_1) 10

Open for

Admission to Single Courses at the Faculty of Science and Technology
Industrial Asset Management - Master of Science Degree Programme
Exchange programme at Faculty of Science and Technology

Course assessment

There must be an early dialogue between the course supervisor, the student union representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital subject evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.


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