Discrete-event Simulation in Production and Logistics

  • type: Lecture
  • chair: Nickel
  • semester: Master
  • place:

    Room 221, Bdg. 11.40 (Kollegien am Ehrenhof)

     
  • time: Wednesday, 11:30-13:00 and 14:00-15:30
  • start: 24.04.2013
  • lecturer: Dr. Sven Spieckermann
  • ects: 4,5 LP
  • exam: Assessment consists of a written paper and an oral exam
  • information:

    The lecture takes place 6x on Wednesdays in the slots 11:30-13:00 and 14:00-15:30.
     
    Schedule:
     
    - Wednesday, 24.04., 11:30-13:00 and 14:00-15:30
    - Wednesday, 08.05., 11:30-13:00 and 14:00-15:30
    - Wednesday, 22.05., 11:30-13:00 and 14:00-15:30
    - Wednesday, 05.06., 11:30-13:00 and 14:00-15:30
    - Wednesday, 19.06., 11:30-13:00 and 14:00-15:30
    - Wednesday, 03.07., 11:30-13:00 and 14:00-15:30
     
    Application:

    Due to limited capacity application via E-Mail to dunke@kit.edu is required. Only admitted participants will be granted access to course materials and can participate in the semester assignment. Applications will be considered according to the first-come-first-served-principle. In case there are more applications than free space a waiting list will be used.

Contents

Simulation of production and logistics systems is an interdisciplinary subject connecting expert knowledge from production management and operations research with mathematics/statistics as well as computer science and software engineering. With completion of this course, students know statistical foundations of discrete simulation, are able to classify and apply related software applications, and know the relation between simulation and optimization as well as a number of application examples. Furthermore, students are enabled to structure simulation studies and are aware of specific project scheduling issues.

The course covers basic concepts of discrete event simulation models and qualifies students for the computer-based usage of simulation systems. This enables students to structure simulation studies according to process models. Additionally, students deepen their knowledges for logical issues and discover the importance of statistical methods in in modeling and evaluation of simulation models. Students gain insight to coupled systems of simulation and meta-heuristics, and they are able to characterize simulation programs.