Syllabus academic year 2011/2012
(Created 2011-09-01.)
Credits: 7,5. Grading scale: TH. Cycle: A (Second Cycle). Main field: Technology. Language of instruction: The course will be given in Swedish. MION15 overlaps following cours/es: MIO240. Optional for: I5lp, I5pr, M4lp, M4pr. Course coordinator: Assistant Professor Fredrik Olsson,, Production Management. Prerequisites: A Basic Course in Mathematical Statistics, MIO310 Operations Research. Assessment: Partly individual assignments in simulation, partly a group exam as an assignment, and partly an individual exam. The final grade depends on the performance of these parts. Home page:

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

have the skills and abilities to independently perform statistically correct input and output analysis of relevant data. The student should have skills in building simulation models from complex real life production systems. Moreover, the student should be able to analytically analyze simple production systems by using Markov theory. Concrete areas and model types that the student should master include:

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 that it is relevant and that the results can be trusted. The content also includes experimental design, generation of random variables and variates. 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.

Compendium: Excerpts from Laguna M. and J. Marklund, Business Process Modeling, Simulation and Design, Prentice Hall 2005.
Additional compendium.