1. Linear optimization models.
1.1. Introduction: decision optimization, analytics and operations research; formulations; graphical and software-based solution.
1.2. Duality; economic interpretation; optimality conditions; sensitivity analysis; robustness.
2. Discrete optimization models.
2.1. Formulations; graphical solution; linear relaxations; optimality gap.
2.2. The branch and bound method; valid inequalities; applications.
3. Dynamic optimization models.
3.1. Formulations; finite-horizon models; optimality equations; numerical solution; applications.
3.2. Infinite-horizon models; optimality equations; numerical solution; applications.