Event

Discrete-Event System Simulation, 5th Edition

Jerry Banks, John S. Carson, II, Barry L. Nelson and David M. Nicol

Modeling and Simulation is the most widely used operations research / systems engineering technique for designing new systems and optimizing the performance of existing systems. In one way or another, just about every engineering or scientific field uses simulation as an exploration, modeling, or analysis technique. The course is designed to provide students with basic knowledge of modeling and simulation approaches and to provide them with first experience of using a simulation package. The course will focus on modeling and simulation of real-world discrete event systems. Examples of discrete events are customer arrivals at a queue of a service desk, machine failures in manufacturing systems, telephone calls in a call center, etc. Moreover, continuous and hybrid models will be also discussed. Topics include Discrete-Event Simulation, Input Modeling, Output Analysis, Random Number Generation, Verification and Validation, Stochastic Petri Nets and Markov Chains.

Competence Certificate

Depending on the number of participants in the course, the exam will be offered either as an oral exam (20 min), or as a written exam (60 min).

The exam takes place every semester and can be repeated at every regular examination date.

Learning Objectives

Knowledge:

• Demonstrate knowledge about general and specific theories, challenges, algorithms, methods, technologies, and tools related to modelling and simulation
• Demonstrate knowledge of two important classes of simulation:
  • Discrete-event Monte-Carlo simulation,
  • Continuous simulation with ODEs
• Demonstrate knowledge of algorithms necessary to build a simulator

Skills:

• Analyse suitability of an approach/tool for a given modelling problem
• Understand simulation models of various types
• Demonstrate methods and techniques to overcome common challenges in modelling and simulation
• Model simulation input data
• Analyse and model discrete stochastic systems
• Analyse and interpret simulation results

Competences:

• Use different methods to conduct simulation-based analysis of real-world data
• Build and simulate stochastic models
• Use simulation software

Prerequisites

Some experience in programming and knowledge of basic mathematics and statistics

Form of instruction

Lectures and exercises. A detailed course plan will be published before the semester start.