Decision making: Decision support models and their use in decision making; Elements and structure of a decision problem; Decision Trees: Decision Matrices The Bayes, Maximin, Maximax and Hurwitz criteria; Problem-solving with sampling information about the states of nature; Values of sampling and complete information. Dynamic programming: Characteristics of a dynamic programming problem; Examples of multi-stage decisions; Schematic representation of multi-stage decision making problems; Linear programming: Characteristics of a linear programming problem; Modeling mathematically a linear programming problem; Possible solutions to linear programming problems; The graphical solution method; The Simplex algorithm; The dual problem; Sensitivity analysis; Simulation: Special features and schematic presentation of simulation; Generation of random observations through probability distribution functions; Time increment techniques; Simulation languages; Laboratory exercises using appropriate software.