Monte Carlo and Discrete Event Simulation

Monte Carlo and Discrete Event Simulation are two powerful classes of simulation methods that have a broad application range.

Monte Carlo simulation is a set of stochastic algorithms for sampling, estimation and optimization, applied in a wide range of fields such as engineering, finance, computer science, and the physical and life sciences. An example application is the estimation / management of risk related to the cost or the schedule of a project.

In Discrete Event Simulation, the time evolution of a system is modelled as a series of discrete events. Discrete event simulation can for example be used to find the bottleneck in a production line or to improve patient flow in a hospital.

In this course, the student will learn to conduct simulation experiments with Monte Carlo methods and Discrete Event Simulation. All aspects of computer simulation will be covered: system-modelling, implementation of algorithms, running simulations, visualization and interpretation / analysis of results.

Course topics include:

• Random Number Generation.

• Random Variates.

• Random Processes.

• Markov Chain Monte Carlo.

• Queuing.

• Statistical Analysis of Simulation Data.

All topics are taught from low-level (theoretical understanding and low-level programming) to high-level (practical application to cases from industry and use of existing software). Relevant simulation examples will be given from different fields of science and industry.

Knowledge:

- Be familiar with possible applications of Monte Carlo and Discrete Event Simulation.

- Be able to explain in detail:

• how one can sample random variates and random processes,

• Markov chain Monte Carlo,

• how one can simulate queuing,

• methods for statistical analysis of simulation results.

Skills:

• To be able to conduct simulation experiments with Monte Carlo and Discrete Event methods, including system modelling, implementation of algorithms, running simulations, visualization, analysis and interpretation of results.

General Competence:

• Be able to reason which simulation method is best for a given system.

• Be able to recognize and understand implementations of Monte Carlo and Discrete Event Simulation.

Lectures, video lectures, classroom exercises, 2-4 mandatory project assignments. The 2-4 mandatory project assignments have to be handed in and approved by the lecturer in order to get access to the exam.