## Introduction to Operations Research |

### From inside the book

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Page 26

concern. The adjective linear means that all the mathematical functions in this

model are required to be linear functions. The word programming does not refer

here to ...

**Linear programming**uses a mathematical model to describe the**problem**ofconcern. The adjective linear means that all the mathematical functions in this

model are required to be linear functions. The word programming does not refer

here to ...

Page 32

When you finish a

homework by choosing the print command under the File menu. To use this

routine, choose General

choose ...

When you finish a

**problem**, you can print out everything you have done for yourhomework by choosing the print command under the File menu. To use this

routine, choose General

**Linear Programming**under the Area menu. Thenchoose ...

Page 131

And, with the help of both mathematical programming modeling languages and

improving computer technology, this now is becoming common practice. Until the

mid-1980s,

And, with the help of both mathematical programming modeling languages and

improving computer technology, this now is becoming common practice. Until the

mid-1980s,

**linear programming problems**were solved almost exclusively on ...### What people are saying - Write a review

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### Contents

INTRODUCTION | 1 |

OVERVIEW OF THE OPERATIONS RESEARCH | 8 |

INTRODUCTION TO LINEAR PROGRAMMING | 25 |

Copyright | |

22 other sections not shown

### Other editions - View all

Introduction to Operations Research Frederick S. Hillier,Gerald J. Lieberman No preview available - 1995 |

### Common terms and phrases

algorithm apply automatic routine basic solution calculate changes coefficients column concave function Consider the following constraint boundary convex corresponding cost Courseware CPF solution decision variables dual problem dynamic programming entering basic variable equations estimate example exponential distribution feasible region feasible solutions following problem forecast formulation functional constraints Gaussian elimination given identify initial BF solution integer IP problem iteration leaving basic variable linear programming model linear programming problem LP relaxation Markov chain matrix Maximize Minimize mixed strategy node nonbasic variables nonlinear programming nonnegative number of customers objective function obtained optimal policy optimal solution parameters player presented in Sec primal problem Prob probability distribution procedure profit queueing models queueing system queueing theory random numbers resulting sensitivity analysis servers simplex method simulation slack variables solve strategy subproblem tion transportation problem trial solution unit Wyndor Glass zero