1) Selected pieces of knowledge of probability theory - generating functions, random variables used in the queueing theory, convolution.
2) Baysian statistical principles.
3) Stochastic programming.
4) Theory of random processes with continuous and discrete time.
5) Advanced knowledge of the queueing theory - methods of input flow modeling, methods of modeling of the operating time, methods of calculation of performance measures.
6) Markov queueing systems and methods of their modeling in time (transition analysis).
7) Markov queueing systems and their modeling methods in steady state.
8) Modeling of queueing systems with Erlang input flow and / or Erlang service time.
9) Modeling of M/D/1, M/G/1 and G/M/1 queueing systems.
10) Multi-operator systems with service lines that do not work continuously (due to malfunctions, maintenance, etc.).
11) Queueing networks and their modeling.
12) Possibilities of computer modeling of queueing systems (Witness, colored Petri nets).
2) Baysian statistical principles.
3) Stochastic programming.
4) Theory of random processes with continuous and discrete time.
5) Advanced knowledge of the queueing theory - methods of input flow modeling, methods of modeling of the operating time, methods of calculation of performance measures.
6) Markov queueing systems and methods of their modeling in time (transition analysis).
7) Markov queueing systems and their modeling methods in steady state.
8) Modeling of queueing systems with Erlang input flow and / or Erlang service time.
9) Modeling of M/D/1, M/G/1 and G/M/1 queueing systems.
10) Multi-operator systems with service lines that do not work continuously (due to malfunctions, maintenance, etc.).
11) Queueing networks and their modeling.
12) Possibilities of computer modeling of queueing systems (Witness, colored Petri nets).