Stochastic Modeling & Simulation
This course examines on one hand the fundamental types of stochastic models employed within Management Science and, on the other hand, the use of simulation techniques in cases where stochastic methods are of limited applicability. In addition, it discusses the application of all the above in real settings of decision support, using simulation software packages.
Stochastic modeling includes mostly Markov processes and Markov chains, while also examining topics in Queuing Theory, Replacement Theory and basic principles of Stochastic Dynamic Programming.
Simulation refers mostly to discrete event simulation, while presenting also techniques for model building and validation and analysis of simulation output. Emphasis is given to the construction of simulation models through appropriate software packages, hence part of the course is implemented via lab exercises and tutorials and through a compulsory project which includes all steps of applying simulation on a real-life problem.
The course material includes the following topics:
- Markov processes and chains
- Queuing Theory, Replacement Theory, Inventory Theory
- Stochastic dynamic programming
- Simulation as an experimental methods, applications in Management Science, basic simulation techniques
- Discrete even simulation, entities and activities, events and queues, resources activity cycle diagrams
- Simulation languages and packages, the SIMUL8 software
- Transient and steady state, input and output analysis, random-number generators, experimentation principles
- Simulation modeling of real-life applications, case-study discussion