OR 641

STOCHASTIC MODELS

FOR OPERATIONS RESEARCH  -  I

COURSE INFORMATION

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This is the first of the three course sequence in stochastic models. However, this can also serve as a standalone introductory course in stochastic models. The course will cover the following major topics:

1. Discrete Time Markov Chains (13 Lectures)

2. Poisson Processes (4 Lectures)

3. Continuous Time Markov Chains (8 Lectures)

4. Special Topics (2 Lectures)

Text:

Modeling and Analysis of Stochastic systems,

by V. G. Kulkarni, 1995, CRC Press. (Download errata)

References:

1. Stochastic Processes, by S. M. Ross,
2. Stochastic Models in Operations Research, by D. P. Heyman and M. J. Sobel,
3. Introduction to Stochastic Processes, by E. Cinlar,
4. A First Course in Stochastic Processes, by S. Karlin and H. Taylor.

Prerequsites

The course evaluation will be based on

1. Ten best out of 12 to 13 homeworks (20%)
2. Mid-term (40%)
3. Final (40%) 

The mid-term is take home, while the final is in class. Both are open-book, open-notes.