Course ObjectiveStudents learn how to model real-life problems by discrete-event models.
After successful completion of this course, students will be able to
conduct Monte Carlo simulation based analysis of a problem and provide
an output analysis. Students learn how to apply simulation in
optimization and learning, and to report on their findings.
Course ContentThis course gives a treatment of the important aspects of advanced Monte
Carlo simulation and its applications in areas such as inventory
control, project planning, reliability, risk analysis, multi-agent
models, and financial models. The emphasis is on modeling the stochastic
dynamic system as a discrete event system, and analyzing and improving
its performance by means of discrete event simulation. The topics
covered include generating random numbers, variance reduction methods,
Markov chain Monte Carlo methods, selecting input distributions, and
model validation. The course also teaches the statistical output
analysis and the use of simulation in optimization and learning.
Teaching MethodsCombined lectures and tutorials
Method of AssessmentFinal exam – Individual assessment
Individual assignment - Individual assessment
Entry RequirementsNumerical Methods (or comparable course)
LiteratureThe essential literature is provide during the lectures.
Recommended literature is
Chapters 1,2,5,6,7,8,9 of [Averill Law: Simulation Modeling and
Analysis, Mc Graw Hill 4-th or 5-th ed]
Chapter 11 of [Cassandras and Lafortune: Introduction to Discrete Event
Systems, Springer, 2nd ed 2008]
Recommended background knowledgeAnalysis, basic probability theory, basic programming
|Language of Tuition||English|
|Faculty||School of Business and Economics|
|Course Coordinator||prof. dr. B.F. Heidergott|
|Examiner||prof. dr. B.F. Heidergott|
You need to register for this course yourself
Last-minute registration is available for this course.
|Teaching Methods||Study Group, Lecture|
This course is also available as: