DETERMINISTIC OPTIMIZATION (3 Credits)

Learning Outcomes:

On successful completion of this course, student will be able to: LO1 – Identify objectives and constraints based on problem descriptions; LO2 – Create mathematical optimization models; LO3 – Apply an understanding of the techniques used to solve linear optimization models using their mathematical structure; LO4 – Use optimization software to conduct analyses and interpret the output; LO5 – Create recommendations based on solutions, analyses and model’s limitations.

Topics:

1. Transportation Problems;
2. Duality Theory 2;
3. Network Models 2;
4. Sensitivity Analysis;
5. Various Types of LP Models;
6. Sensitivity Analysis 2;
7. Graphical Method for two variable LP;
8. Solving Integer Programming 2;
9. Assignment Problems;
10. Network Models;
11. Simplex Algorithm;
12. Solving Integer Programming;
13. Modeling Integer Programming;
14. Transshipment Problems;
15. Linear Programming;
16. Duality Theory;
17. Modeling Integer Programming 2;
18. Sensitivity Analysis using Graphical Method.