Syllabus academic year 2007/2008
|SIMULATION OF PRODUCTION SYSTEMS||MIO240|
Higher education credits: 6.
Grading scale: TH.
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/.
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
- in-depth studies in quantitative methods for simulation of production systems.
- practice and development of the ability to lead (formulate and solve) an industrial project in simulation.
Knowledge and understanding
For a passing grade the student must
For the simulation section of the course this means:
- be able to use queuing theory/Markovian theory and methodology for discrete event simulation modelling, to analyse, and solve business problems relating to operational and managerial decisions.
For the theoretical section of the course this means:
- to get in-depth understanding for the principles of discrete event simulation modelling, and the opportunities and limitations this technique offers.
- to be able to use a commercial software (Extend) to create a simulation model and use this to analyse discrete event systems and processes.
- to be able to correctly use statistical methods to analyse input to, and output from simulation models, and to interpret the generated results. This involves the choice and fitting of distribution functions, as well as using various types of hypothesis testing methods.
- to be able to formulate relevant business problems characterised as queuing problems in networks.
- to calculate steady state probabilities for the Markovian systems
- to be able to interpret the solutions and results and place them in a business context.
Skills and abilities
be able to independently formulate, solve and interpret:
For a passing grade the student must
- Markovian processes,
- queuing models in network,
- discrete event simulation models (modelled in the software Extend)
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 sections 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.
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)