1) Queueing theory and Markov chains.
General characteristics of queuing systems. Notation and examples. Little's law. Simple queuing models (M/M/1, M/M/c models with limited
capacity). Queuing models with general distributions (M/G/1, GI/M/1, GI/G/1). Open queuing networks (tandem queues, networks of Jackson).
Closed queuing networks. Petri Nets (notation, general properties of Petri Nets, applications and examples). Introduction to Markov Chains
(memoryless random variables, stochastic processes). Markov chains in discrete and continuous time. Birth and death processes (BD) and
absorbing states. Examples of applications in production contexts and services.
2) Advanced optimization techniques
Definition of optimization problems (general formulation, local optimum, local optimum discrete global optimum, instances of a problem). Simulation of production-service systems. Notes on the theory of computational complexity. Definition of heuristics (general definition, Greedy Heuristics, Metaheuristics) and classification of Metaheuristics. Notes on the measurement of the performance of the algorithms. Applications in industrial environments