Stochastic Simulation

• Apply build-in random number generators in various software products
• Implent algorithms for random number generation from various distributions
• Perform simple statistical analysis of simulated data
• Apply simulation based statistical techniques like bootstrap and Markov Chain Monte Carlo
• Apply simulated annealing as a heuristic method for minor optimization problems with discrete variables
• Develop and implement simulation procedures for simple technical systems using the event by event principle
• Perform verification of a programme for simulation
• Perform validation of a simulation model
• Plan and execute a simulation study for a specific part or function of some (technical) system
• Apply techniques for variance reduction in a simulation study
• Present the results of a simulation study writtenly or verbally

The first six to seven days deals with theory and exercises, followed by a case-study during the remaining part of the last two weeks. Attendance is mandatory for the first period of the course.
Theory: The modelling process, methods of solution, random number generation, sampling from statistical distributions. Discrete simulation: Time-true simulation, event-by-event principle, variance reduction. Case-studies: Operations research real-life problems, e.g. performance of data- and telecommunication systems, production planning, inventory control, optimal stochastic control etc.

02402, or a similar course in elementary statistics. Basic programming skills.

Lectures, exercises and project work.