GRA 4138 Business Simulation Analysis
The course objective is to familiarize the students with computer-based simulation methodologies and tools used in business settings characterized by uncertainty and variability. The main focus will be on discrete-event, agent-based and Monte Carlo simulations.
- To identify, argue, and interpret the use of simulation models, including discrete-event, agent-based, and Monte-Carlo, in real-world cases
- To model and analyse various systems using discrete-event simulation, agent-based simulation and Monte-Carlo techniques and tools
- To interpret and demonstrate outputs of the models effectively
- To carry out sensitivity analysis, optimization and calibration by using simulation
- To build and visualize simulation models and experiments in AnyLogic
- To identify real-world problems to which simulation methods are useful and relevant
- To appreciate and examine the way uncertain and variable factors affect the performance of various systems and how simulation helps to model, design, improve and analyze such systems
- Introduction to simulation
- Monte-Carlo simulation
- Anylogic modeling environment
- Discrete-event simulation
- Agent-based simulation
- Calibration, optimization and parameter variation experiments
- Pedestrian, process, material handling, and traffic modeling libraries in Anylogic
- Simulation in practice
- The course consists of lectures, exercises, presentations, and discussions.
- The course is mostly held in the form of learning-by-doing.
- Students build different models in Anylogic step-by-step in the class and extend the models out of the class.
- There will be guest lectures by practitioners about real-world applications of simulation.
Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class.
Students must have a computer with them in all of the lectures. Anylogic (Personal Learning Edition) must be installed on the computers.
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.
- Basics of probability and statistics theory
- Knowing the basics of at least one programming language
- Although it is not a mandatory prerequisite, and students will learn as much programming (Java) as they require during the course, some basic experience with Java programming language could be useful
|Exam category||Weight||Invigilation||Duration||Grouping||Comment exam|
Form of assessment:
Examination when next scheduled course
|100||No||1 Semester(s)||Group (1 - 3)||Term paper (Project) Groups with fewer than 3 members will be allowed only in exceptional cases.|
|Form of assessment:||Written submission|
|Grouping (size):||Group (1-3)|
|Comment:||Term paper (Project) Groups with fewer than 3 members will be allowed only in exceptional cases.|
|Exam code:||GRA 41381|
|Resit:||Examination when next scheduled course|
Prepare for teaching
Individual problem solving
Student's own work with learning resources
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.