1) Introduction to modelling and simulation.
2) Basic description of Witness simulation software.
3) Introduction to discrete simulation, event-based algorithms, activity-based algorithms.
4) Methods of generating pseudo-random numbers.
5) Methods of transformation of pseudo-random numbers.
6) Introduction to estimation theory, point estimation of parameters.
7) Interval estimations of mean value.
8) Introduction to statistical hypothesis testing, normality testing - Pearson\'s goodness-of-fit test.
9) One sample mean value test.
10) Two sample mean value test.
11) Introduction to linear programming, Solver in Microsoft Excel, Xpress-IVE optimization software.
12) Transportation problem and its solution.
13) Location and allocation tasks and their solution.
14) Reserve.
2) Basic description of Witness simulation software.
3) Introduction to discrete simulation, event-based algorithms, activity-based algorithms.
4) Methods of generating pseudo-random numbers.
5) Methods of transformation of pseudo-random numbers.
6) Introduction to estimation theory, point estimation of parameters.
7) Interval estimations of mean value.
8) Introduction to statistical hypothesis testing, normality testing - Pearson\'s goodness-of-fit test.
9) One sample mean value test.
10) Two sample mean value test.
11) Introduction to linear programming, Solver in Microsoft Excel, Xpress-IVE optimization software.
12) Transportation problem and its solution.
13) Location and allocation tasks and their solution.
14) Reserve.