DETERMINISTIC OPTIMIZATION & STOCHASTIC PROCESSES (4 Credits)

Learning Outcome:

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

Topics:

Various Types of LP Models, Simplex Algorithm, Transportation Problem, Network Models, Modeling Integer Programming, Probability and Random Variables, Discrete-Time Markov Chains, Continuous-Time Markov Chains, Renewal Process, Queuing Theory