Video-Server > Vorlesungen > Computational Modeling and Simulation I: Discrete Systems

by Prof. Dr.-Ing. Dietmar P. F. Möller

Semester: summer semester 2015

Course number: S 0503

The lecture give an introduction into the theoretical and methodological background of discrete-event systems modeling and simulation. Based on this knowledge applications of discrete event systems simulation case study examples will be introduced to show how to apply the gained knowledge in an area of concentration. This require at the very first a clear problem formulation, setting of objectives and an overall project plan, model conceptualization, data collection, model translation and verification, and finally simulation runs and validation of obtained results. Therefore the class introduce into the basics of simulation with a specialization on concepts in discrete-event simulation. Based on that simulation software tools will be introduced to link with the execution of mathematical models and integration algorithms. With this knowledge the analysis of simulation data as well as random number generation, queuing systems and Monte Carlo simulation will be introduced and discussed in the very detail based on case study examples.

Additional information about the lecture:

Institut für Angewandte Stochastik und Operations Research or in the directory of lectures

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Lecture no. 2

01:00 hrs18.Oct.201646 Views

**Topics:**

Overview and Learning Scope, Introduction, Terminology and Concepts

Lecture no. 3

01:04 hrs18.Oct.201622 Views

**Topics:**

Statistical Models, Distributions, Summary and Exercises, References and Further Readings, Outlook

Lecture no. 4

01:23 hrs18.Oct.201616 Views

**Topics:**

Overview and Learning Scope, Introduction, Characteristics of Queuing Systems, Queuing Notation

Lecture no. 5

01:16 hrs18.Oct.201616 Views

**Topics:**

Steady State Behavior of Infinite and Finite Population Models, Networks of Queues, Summary and Exercises, References and Further Readings, Outlook

Lecture no. 6

01:25 hrs18.Oct.20169 Views

**Topics:**

Overview and Learning Scope, Introduction, Input modeling, Data collection