The course sets out to develop the following competences: 1) Capacity to formulate data-based analytics models for optimal decision making (operations research) in diverse applications; 2) capacity to analyze such models based on an understanding of their properties; 3) capacity to obtain numerical solutions for such models through computer software; 4) capacity to interpret the numerical solutions obtained in terms of optimal decisions.

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. 1.3. Applications. 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.

Theoretical-practical classes with web-based supporting material. Computational sessions with numerical software. The teaching methodology will have an eminently practical approach, being based on the formulation and solution of decision optimization models from diverse application areas. Weekly individual tutorials will be scheduled.