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Neha Tikoo, Lovely Professional University Unit 2: Linear Programming Problems

Unit 2: Linear Programming Problems Notes

CONTENTS
Objectives
Introduction

2.1 Basic Terminology
2.2 Application of Linear Programming
2.3 Advantages and Limitations of Linear Programming

2.4 Formulation of LP Models
2.5 Maximization Cases with Mixed Constraints
2.6 Graphical Solutions under Linear Programming
2.7 Minimization Cases of LP
2.8 Cases of Mixed Constraints

2.9 Summary
2.10 Keywords
2.11 Review Questions

2.12 Further Readings
Objectives

After studying this unit, you will be able to:
 Understand what is linear programming
 Locate areas of application with its scope

 Know how to formulate LP models and use graphical procedure to solve them

Introduction

Each and every organization aspires for optimal utilization of its limited scarce resources like
men, money, materials, machines, methods and time to reach the targets. The results are generally
measured in terms of profits, losses, return on money invested, etc. To achieve these results, the
decision-maker has to have thorough knowledge about the tasks or jobs and the relationships
among them. Among the popular techniques of Operations Research, Linear Programming
deserves mention because it is one of the widely used techniques. And it is a deterministic
model. In other words, Linear Programming is one of the important Operations Research tools
used to allocate scarce resources in an optimal way so that the allocator can optimize the results
either by maximizing the profits or minimizing the costs. The credit of innovating this technique
goes to George B. Dantzig. He innovated this technique while he was working for U.S. Air force
during World War II, 1947. Initially, this technique was used to solve tough logistic problems
like assignment and transportation but instantly the application of this technique has spread to
almost every functional area of management, production planning and control, personnel
management, advertising and promotion.

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