Syllabus academic year 2007/2008
SIMULATION OF PRODUCTION SYSTEMSMIO240

Higher education credits: 6. Grading scale: TH. Level: A (Second level). Language of instruction: The course will be given in Swedish. Optional for: I4lp, I4pr, M4, M4lp, M4pr. Course coordinator: Stefan Vidgren, Stefan.Vidgren@iml.lth.se, Produktionsekonomi. Prerequisites: A Basic Course in Mathematical Statistics, and one of the courses MIO310 Operations Research or MTT091 Material Handling.. Assessment: Partly a group exam as an industrial assignment, and partly an individual oral exam. The final grade depends on the performance of these parts. Home page: http://www.iml.lth.se/pm/.

Aim
The course has the overarching theme of simulation and aims to provide further knowledge in deterministic and stochastic modelling of operational and managerial business problems.

Concrete goals in the course

Knowledge and understanding
For a passing grade the student must

For the simulation section of the course this means:

For the theoretical section of the course this means:

Skills and abilities
For a passing grade the student must

be able to independently formulate, solve and interpret:

Contents
The simulation section of the course examines Markovian theory as an analytical tool for analysing stochastic systems. To deal with more complex systems, a commercial software for discrete event simulation (Extend) is used. The developed models are used for analysing and improving production processes, and flow of materials and information. In order to arrive at a relevant simulation model, various types of stochastic events and processes must be characterised by appropriate distribution functions. Moreover, the output data from the simulation model must be analysed statistically in a correct way. Another important aspect is how to verify and validate the model to assure it is relevant and the results can be trusted. The section’s mandatory project assignment is structured around a case study dealing with the analysis of a small production system using simulation models. The objective is to provide an understanding for the strengths and weaknesses with discrete event simulation models as a tool for process analysis. Each project group reports their assignment work and the obtained results in a well structured technical report.

Literature
Laguna M. and J. Marklund, Business Process Modeling, Simulation and Design, Prentice Hall 2005. (Includes a CD with the software Extend and other complementary material)
Course compendium.