LEARNING OBJECTIVES

• Basic terminology: operations research, mathematical optimization, and mathematical programming
• Basic elements of optimization models: data, decision variables, objective functions, and constraints
• Different types of solution: feasible, optimal, infeasible, and unbounded
• Mathematical programming techniques for optimization: Linear Programming, Integer Programming, Mixed Integer Programming, and Quadratic Programming
• Algorithms used for solving continuous linear programming problems: simplex, dual simplex, and barrier
• Important mathematical programming concepts: sparsity, uncertainty, periodicity, network structure, convexity, piecewise linear and nonlinear

Syllabus

• What is Operations Research?
• What is Optimization?
• Optimization Models

• Introduction to Linear Programming
• A Production Problem : Part 1 - Writing the model
• A Production Problem : Part 2 - Finding a solution
• A Production Problem : Part 3 - From feasibility to unboundedness
• Algorithms for Solving Linear Programs : Part 1 - The Simplex and Dual Simplex Algorithm
• Algorithms for Solving Linear Programs : Part 2 - The Simplex and Barrier methods

• Introduction to Network Models
• The Transportation Problem
• The Transshipment Problem
• The Assignment Problem
• The Shortest Path Problem
• Critical Path Analysis

• Nonlinearity and Convexity
• Piecewise Linear Programming
• Integer Programming
• The Branch and Bound Method
• Quadratic Programming

• Modelling in the Real World
• The Importance of Sparsity
• Tips for Better Models

General Information

• This course is self-paced.
• It can be taken at any time.

RECOMMENDED SKILLS PRIOR TO TAKING THIS COURSE

• Basic understanding of Cloud Computing (the concept).  It is also helpful if you know Javascript, but not required