1. Linear optimization models.
1.1. Introduction: decision optimization, analytics and operations research; formulations.
1.2. Graphical solution; sensitivity analysis; software-based solution.
1.3. Duality; economic interpretation; optimality conditions; sensitivity analysis.
2. Discrete optimization models.
2.1. Formulations; graphical solution; linear relaxations; optimality gap.
2.2. The branch and bound method; strengthening formulations; valid inequalities; software-based solution.