Students are expected to have completed courses with contents in linear algebra, statistics, business administration and computer programming.

Core competences: 1. Modeling decision optimization problems in the framework of Operations Research models. 2. Formulating, analyzing and solving linear optimization models, by the graphical method, the simplex method and computer software (in particular, spreadsheets). 3. Formulating, analyzing and solving integer optimization models, by the graphical method, the branch and bound method, and computer software. 4. Formulating, analyzing and solving queueing models of M/M/m type. 5. Designing and performing computer simulation experiments by the Monte Carlo method. Transversal competences: 1. Capacity for analysis and synthesis. 2. Mathematical modeling and problem solving. 3. Oral and written communication.

-Topic 1. Linear optimization (LO). 1.1. Introduction to Operations Research; LO models, formulations, applications and computer-based solution. 1.2. Graphical solution and sensitivity analysis. 1.3. The fundamental theorem of LO; basic feasible solutions and vertices; the simplex method. 1.4. Problems with unbounded objetive; the two-phase simplex method. 1.5. Duality in LO; economic interpretation and application to sensitivity analysis. 1.6. Optimal network flow models. -Topic 2. Integer optimization (IO). 2.1. IO models and applications; linear relaxations; optimality gap; optimality test; graphical and computer-based solution. 2.2. The Branch and Bound method. 2.3. Combinatorial optimization models. Strengthening formulations with valid inequalities. -Topic 3. Queueing theory (QT). 3.1. QT models and applications; performance metrics; utilization factor and stability; Little's law; PASTA property. 3.2. The M/M/1 model; calculation of performance metrics. 3.3. The M/M/m model; calculation of performance metrics. -Topic 4. Simulation. 4.1. Simulation models; Monte Carlo method and applications; computer generation of pseudo-random numbers. 4.2. Computer generation of discrete and continuous probability distributions.

Theory (3 ECTS). Theory classes with supporting material available in the course web page. Practical classes (3 ECTS). Problem-solving classes. Computer labs. Weekly individual tutoring sessions.