1. Queuing theory and production processes: general characteristics of queue systems. Notation and examples. Simple queue models (M/M/1, M/M/c, limited capacity models). Little's law. Queuing models with general distributions (M/G/1, GI/M/1, GI/G/1).
2. Markov chains: random memoryless variables and stochastic processes. Discrete and continuous time Markov chains. Birth and death processes (BD) and absorbing states. Examples of applications in production and service contexts. Open queueing networks (tandem code, Jackson networks). Closed queuing networks.
3. Petri nets: notation, general properties of Petri nets, applications and examples to production and service systems.
4. The discrete event simulation of production systems: model setting, generation of random numbers, performances. Validation of the models. The hybrid simulation. Report planning and structuring. Application cases. Introduction to the use of Anylogic discrete event simulation software. Use of simulation in optimization problems: general formulation of optimization problems.