Use of deterministic and stochastic mathematical models to study and optimize systems. This course includes an introduction to mathematical modeling and the study of linear programming, network models, Markov processes and queuing theory. Students will use computer software for model construction and problem solving.
Prerequisites:
Credit in calculus, probability, statistics, or permission of instructor.
Course Objectives:
Analyze problem situations and identify the relevant decision variables, parameters, constraints and performance measures associated with those situations.
Identify and apply appropriate modeling techniques for general classes of problems. Techniques include stochastic and deterministic algorithms such as linear programming, queuing analysis and Markov processes.
Required Topics (approximate weeks allocated):
Introduction to mathematical modeling (1)
Terminology
Classification of models
Linear Programming (3)
Formulation
Simplex Method
Sensitivity Analysis
Network models (3)
Network representations
Algorithms for shortest path, minimum spanning tree, maximum flow
Markov processes (3)
Classification of states
Steady-state probabilities
Absorbing states
Queuing theory (4)
Birth death models
Non-birth death models
Queuing networks
Exams (1)
Department of Computer Science and Software Engineering
