The student, upon completion of this course, will be able to:
- Describe (identify/write) the underlying concepts of linear programming that can be used to model complex decision problems for identifying an optimal solution.
- Develop the theory behind simplex method for solving linear programming problems
- Show how the two-phase and big-M methods can be used to solve linear programming problems that involve equality and/or greater-than-or-equal to constraints
- Develop the theory for dual problems, and establish the relationships to its primal counterpart.
- Show how sensitivity analysis can be used to investigate into the changes to one or more model parameters.
- Demonstrate the importance of general, binary, and mixed-integer linear programming problems, and show how they can be implicitly solved using branch-and-bound algorithms.