GRA 4138 Business Simulation Analysis

GRA 4138 Business Simulation Analysis

Course code: 
GRA 4138
Accounting and Operations Management
Course coordinator: 
Mehdi Sharifyazdi
Course name in Norwegian: 
Business Simulation Analysis
Product category: 
MSc in Business Analytics
2025 Spring
Active status: 
Level of study: 
Teaching language: 
Course type: 
One semester

This course offers a comprehensive exploration of business simulation techniques, focusing on their application in uncertain and variable environments. Students will gain hands-on experience with Discrete-Event, Agent-Based, and Monte Carlo simulations, learning to navigate the complexities of business decision-making with computer-based simulation tools.

Learning outcomes - Knowledge
  • Understand the principles and applications of Discrete-Event, Agent-Based, and Monte Carlo simulations in addressing real-world business challenges.
  • Critically evaluate simulation models and their effectiveness in various scenarios.
Learning outcomes - Skills
  • Design, analyze, and implement simulation models using Discrete-Event, Agent-Based, and Monte Carlo approaches.
  • Skillfully interpret simulation outputs and communicate findings.
  • Conduct parameter variation, optimization, and calibration experiments on simulation models.
  • Create and present visual simulations and experiments within the AnyLogic environment.
General Competence
  • Recognize and formulate solutions to business problems where simulation methodologies offer strategic value.
  • Assess the impact of uncertainty and variability on system performance and leverage simulation for system design, improvement, and analysis.
Course content
  • Fundamentals of different simulation approaches in business decision-making.
  • Techniques and applications of Monte-Carlo simulation.
  • Introduction to and advanced features of the AnyLogic modeling environment.
  • Techniques and applications of Discrete-Event simulation.
  • Techniques and applications of Agent-Based simulation.
  • Methods for calibration, optimization, and managing parameter variations.
  • Exploration of the pedestrian and other specialized libraries within AnyLogic.
  • Practical insights into simulation deployment in professional settings.
Teaching and learning activities
  • Engaging lectures intertwined with hands-on exercises to reinforce learning.
  • Active learning through the construction and modification of AnyLogic models, guided by step-by-step instructions for tackling new challenges.
  • Integration of presentations, exercises, and discussions to foster a dynamic learning environment.
  • Insightful guest lectures from industry experts showcasing the real-world impact of simulation.
  • The course predominantly employs AnyLogic Personal Learning Edition (PLE) for a comprehensive exploration of simulation concepts, including Discrete-Event and Agent-Based simulations, experiments, and even some aspects of Monte Carlo simulations. While AnyLogic serves as the cornerstone for nearly all course activities, Excel-based models will also be utilized, albeit on a smaller scale, for delving into Monte Carlo simulations.
Software tools
Software defined under the section "Teaching and learning activities".
Additional information
  • Please be aware that although attendance is not mandatory for all courses, students are responsible for acquiring any information disseminated during class sessions.
  • It is essential for students to bring a computer to every lecture. The computer must have AnyLogic (Personal Learning Edition) installed for course-related activities.

All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.


Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.

Required prerequisite knowledge
  • A foundational understanding of probability and statistics theory.
  • Familiarity with the basics of at least one programming language.

Although students will acquire the necessary Java programming skills throughout the course as needed, and while it is not a requirement, some preliminary experience with the Java programming language could be beneficial.


Exam category: 
Form of assessment: 
Submission PDF
Exam/hand-in semester: 
First Semester
Group (1 - 3)
1 Semester(s)
Term paper (Project)
The preferred group size will be announced based on the number of course enrollments.
Exam code: 
GRA 41381
Grading scale: 
Examination when next scheduled course
Type of Assessment: 
Ordinary examination
Total weight: 
Student workload
Prepare for teaching
54 Hour(s)
Students must learn the basics of simulation theories and AnyLogic skills and libraries through asynchronous lectures.
In addition, they will make basic models based on the asynchronous lectures to be extended and developed further in the class.
74 Hour(s)
Working on the term (group) project which is the same as the examination
Individual problem solving
24 Hour(s)
Working on the assignments and challenges given in both synchronous and asynchronous lectures
Student's own work with learning resources
8 Hour(s)
Reading the uploaded papers
Sum workload: 

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.