IIND 2104 Modelos Probabilísticos
This course is intended to provide students with a sound preparation in stochastic processes basic concepts, and queuing and simulation theory, which enable them to better understand and use non-deterministic models in real problem formulation and solution. The course is divided in two parts: Discrete Time Stochastic Processes and Continuous Time Stochastic Processes.
Objectives
- The students will learn to apply knowledge of mathematics and probability in the design, implementation and analysis of stochastic processes models. Such processes include Continuous - Time Markov Chains, queueing theory, Discrete - Time Markov Chains and stochastic dynamic programming. Through the completion of three individual written exams, we evaluate this ability.
- The students will learn to work in teams on a project in which a real system will be studied, using the topics covered in the course. During the first phase of the project they will need to work in groups in order to understand the system, describe it, and measure its relevant aspects.
- Through the completion of phase II of the project, the students will learn to identify, design and analyze stochastic models in order to measure the performance of a real life system in which randomness is involved. Moreover, they will be able to provide the best answer in terms of performance measures among a set of different modification alternatives aimed at improving the system.
- By presenting the outcomes of the project, the students will learn to communicate the solutions to non - technical decision makers such as high - level managers and factory operators through graphical and visual techniques using common software packages like Microsoft Excel.
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