This lecture provides an introduction and overview of the operations research (OR). First, it begins with the origins of OR, the role as well as the impacts of OR. Later, this lecture also describes the general steps of typical OR studies.

This lecture provides the general features of linear programming. First, it begins by developing a miniature prototype example of a linear programming problem. Next, we present the general linear programming model and its basic assumptions. Lastly, we describe some assumptions in the linear programming problem.

This lecture provides how to solve the linear programming problems with graphical method. This particular graphical method can only be applied if the problem has only two decision variables and therefore only two dimensions. Three exercises are given; and lastly, we discuss some terminologies for solutions of the model with the graphical illustration.

This lecture discusses an introduction to the simplex method to solve the linear programming problems. The augmented form of the problem is firstly discussed. The matrix form of the simplex method is then provided. Lastly, we discuss how to apply the algorithm of the simplex method in a case study. 

The matrix form of the simplex method presented in the previous lecture may be the best one for learning the underlying logic of the algorithm. However, it is not the most convenient form for performing the required calculations. In this lecturer, we describe the "tabular form" of the simplex method which is considered as more convenient and efficient to perform the calculations.

This lecture describes some issues in the simplex method and how to deal with these. The issues include: (i) tie for the entering variable, (ii) degeneracy and cycling, (iii) unboundedness, and (iv) multiple optimal solutions.

In this lecture, we will demonstrate how to solve the linear programming problems using computer softwares, including: Ms. Excel Solver (download the spread sheet here), WolframAlpha, IORTutorial (download here), R-studio, Matlab, and Python.

In this lecture, we will briefly discuss the duality concept, one of the most important discoveries in the early development of linear programming. This discovery revealed that every linear programming problem (called the primal) has associated with it another linear programming problem (called the dual). In addition, we also discuss the dual simplex method, another alternative to solve linear programming problems, which is based on the duality theory.