## Introduction to Operations Research |

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Page 83

of its corner-point solutions lies at the intersection of n constraint boundaries.1 )

Certain 83 pairs of the

other pairs do 4 i / The Essence of not. It will be important to distinguish between

...

of its corner-point solutions lies at the intersection of n constraint boundaries.1 )

Certain 83 pairs of the

**CPF solutions**in Fig. 4.1 share a constraint boundary, andother pairs do 4 i / The Essence of not. It will be important to distinguish between

...

Page 86

86 solution. When there are too many decision variables to find an initial CPF 4 /

Solving Linear solution graphically, this choice eliminates the need to use

algebraic procedures Programming t0 find and solve for an initial

86 solution. When there are too many decision variables to find an initial CPF 4 /

Solving Linear solution graphically, this choice eliminates the need to use

algebraic procedures Programming t0 find and solve for an initial

**CPF solution**.Page 160

solutions. As a consequence, all optimal solutions can be obtained as weighted

averages of optimal

4.5-3 and 4.5-4.) The real significance of Property 1 is that it greatly simplifies the

...

solutions. As a consequence, all optimal solutions can be obtained as weighted

averages of optimal

**CPF solutions**. (This situation is described further in Probs.4.5-3 and 4.5-4.) The real significance of Property 1 is that it greatly simplifies the

...

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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