1) Linear programming problems and their solving
2) Conditions for existence of an optimal solution
3) Stochastic programming - principles, presumptions, applications
4) Stochastic programming models without involving the risk measure
5) Stochastic programming - single stage model with probability constraints
6) Stochastic programming - penalization in the objective function
7) Stochastic programming - models with risk measures
8) Uncertainty and fuzzy sets, basics of fuzzy algebra.
9) Fuzzy optimization - possibilistic mean values, types of uncertainty, alpha-cut approach, possibility, and necessity measures.
10) Flexible programming
11) Introduction to Data Envelopment Analysis (DEA).
12) Basic DEA models and their assumptions.
2) Conditions for existence of an optimal solution
3) Stochastic programming - principles, presumptions, applications
4) Stochastic programming models without involving the risk measure
5) Stochastic programming - single stage model with probability constraints
6) Stochastic programming - penalization in the objective function
7) Stochastic programming - models with risk measures
8) Uncertainty and fuzzy sets, basics of fuzzy algebra.
9) Fuzzy optimization - possibilistic mean values, types of uncertainty, alpha-cut approach, possibility, and necessity measures.
10) Flexible programming
11) Introduction to Data Envelopment Analysis (DEA).
12) Basic DEA models and their assumptions.