1. Provide fundamental knowledge of stochastic models. Develop modeling skills for decisions made under uncertainty.

2. Learn theoretical foundations and application areas of most frequently used stochastic processes and optimization methods. Hence, the acquisition of the following sub-skills is targeted:

a) Model a given process as a Markov chain, calculate system performance measures based on the model, and choose between alternative system configurations and policies.

b) For a given queueing system; calculate performance measures, make improvements according to these performance measures, and choose between alternative queueing system configurations.

3. Ability to make decisions under uncertainty using utility theory and decision trees.