Decision-making in today’s social and business environment has become a complex task. Decision-makers cannot afford to make decisions by simply applying their personal experiences, guesswork, or intuitions, because the consequences of wrong decisions are serious and costly. Hence, an understanding of the applicability of quantitative decision-making methods is of fundamental importance to decision-makers. Operations research is one such tool that helps in decision-making.
Unit 1 deals with transforming a real word decision problem into an operations research model. Solving the linear programming model using graphical and analytical methods.
Unit 2 deals with the application of linear programming techniques in different fields to make their processes more efficient. These include food and agriculture, engineering, transportation, manufacturing, and energy.
Unit 3 the linear-programming models that have been discussed have been continuous, in the sense that decision variables are allowed to be fractional. Often this is a realistic assumption. At other times, however, fractional solutions are not realistic, and we must consider the optimization problem: This problem is called the (linear) integer-programming problem. The purpose of this unit is twofold. First, we will discuss integer-programming formulations. Second, we consider basic approaches that have been developed for solving integer and mixed-integer programming problems.
Unit 4 deals with Multiple criteria decision-making (MCDM), it is considered a complex decision-making (DM) tool involving both quantitative and qualitative factors. In recent years, several MCDM techniques and approaches have been suggested for choosing the optimal probable options. The purpose of this Unit is to systematically review the applications and methodologies of the MCDM techniques like Goal programming and AHP (Analytical hierarchy process).
Unit 5: In many situations, the assumption of linearity as applied to a real-world process might be questionable. This unit defines the basic characteristics of nonlinear programming and explains several useful algorithms employed in solving nonlinear programming problems.
