The Operations Research CU aims to sensitize students to the extensive field of applications of optimization methods. In particular, it is intended to prepare students for modeling and problem solving in areas such as knowledge extraction through network data analysis, resource management and distribution, using linear programming models (PL) and specific algorithms for network structured problems.
Optimization Linear Programming Graphs and Networks Modelling
It is intended that, at the end of this course, the student has acquired the following skills:
Identification of contexts in which PL can be considered;
PL modeling and problem solving using the graphical method, the simplex method and using optimization software;
Formulation and resolution of the dual problem and economic interpretation of the dual variables;
Interpretation and critical analysis of the results;
Rationale for decision making;
Evaluation of the robustness of the solutions through post-optimization and sensitivity analysis in view of the variation of the model parameters;
Formulation and resolution of problems whose models have a network structure;
Identify the main techniques and tools for extracting knowledge from networks;
Use of computational packages to obtain solutions to PL problems.
Introduction; origin, nature and methodology of Operational Research.
Linear Programming (PL): Modeling. Fundamental concepts and results. Resolution methods: use of the XPRESS software; graphic method; simplex method. Identification of an initial permissible basic solution: M-large method. Duality: fundamental theorems of duality; dual simplex method; economic interpretation of the dual. Sensitivity analysis and post-optimization.
Network optimization. Fundamentals of network theory, network models, random graphs, dynamic processes in networks (diffusion and contagion).
Adelaide Cerveira e Maria Manuel Nascimento, Investigação Operacional - Programação Linear. (In Portuguese)
Hillier, F.S., Lieberman, G.J., Introduction to Operations Research, McGraw-Hill, 2005.
Derek Hansen, Ben Shneiderman, Marc A. Smith, Analyzing Social Media Networks with NodeXL: Insights from a Connected World, Ed.: Morgan Kaufmann (2010), ISBN-10: 0123822297.
Evaluation is made on individual basis and it involves the coexistence of two modes: continuous assessment (60%) and
final evaluation (40%). Further information is detailed in the Learning Agreement of the course unit.
