The aim of this course is to enable students to model decision-making problems using analytical methods, solve linear programming problems, and interpret solution results. Within the scope of the course, the graphical method, the Simplex method, duality, and sensitivity analysis will be covered.

Within the scope of this course, the problem-solving process and decision-making problems are examined, and the fundamental concepts of the linear programming approach are introduced. In the first part of the course, emphasis is placed on the assumptions of linear programming, the stages of model formulation, and the construction of objective functions and constraints. The graphical solution of two-variable linear programming problems and special cases encountered in the graphical method are examined in detail. In the continuation of the course, the Simplex method used for solving linear programming problems is covered. Standard form representation, the construction of simplex tableaux, and the iterative solution process are explained through practical applications. In addition, special cases such as infeasibility, multiple optimal solutions, unbounded solutions, and degenerate solutions are evaluated. The course also examines duality theory, primal–dual relationships, and the concept of dual (shadow) prices, while solution interpretations are performed using optimal simplex tableaux. In the final part of the course, sensitivity analyses related to objective function coefficients and right-hand-side coefficients of constraints are discussed in order to evaluate the behavior of solution results under changes in decision-making processes.