Deterministic Optimization & Stochastic Processes (6 Credits)

Learning Outcomes:

Explain objectives and constraints based on problem descriptions in mathematical optimization models;
Apply some methods and the techniques used to solve linear optimization models using their mathematical structure;
Apply the concept of discrete and continuous time Markov chain, transition matrices and state classifications;
Analyze given problems using the concepts of Poisson process, renewal process, or queuing theory.

Topic:

(1) Various Types of LP Models
(2) Simplex Method
(3) Transportation Problems
(4) Network Models
(5) Modeling Integer Programming
(6) Probability and Random Variables
(7) Discrete-Time Markov Chains
(8) Continuous-Time Markov Chains
(9) Renewal Process
(10) Queuing Theory