## Introduction to Operations ResearchCD-ROM contains: Student version of MPL Modeling System and its solver CPLEX -- MPL tutorial -- Examples from the text modeled in MPL -- Examples from the text modeled in LINGO/LINDO -- Tutorial software -- Excel add-ins: TreePlan, SensIt, RiskSim, and Premium Solver -- Excel spreadsheet formulations and templates. |

### From inside the book

Results 1-3 of 59

Page 333

An upper , one - sided

An upper , one - sided

**goal**sets an upper limit that we do not want to exceed ( but falling under the limit is fine ) . 3. A two - sided**goal**sets a specific target that we do not want to miss on either side .**Goal**programming problems ...Page 333

An upper , one - sided

An upper , one - sided

**goal**sets an upper limit that we do not want to exceed ( but falling under the limit is fine ) . 3. A two - sided**goal**sets a specific target that we do not want to miss on either side .**Goal**programming problems ...Page 346

C ( b ) Management is wondering what would happen if the total profit

C ( b ) Management is wondering what would happen if the total profit

**goal**were to be increased to wanting at least $ 140 million ( without any change in the original penalty weights ) . Solve the revised model with this change .### What people are saying - Write a review

Reviews aren't verified, but Google checks for and removes fake content when it's identified

User Review - Flag as inappropriate

i

User Review - Flag as inappropriate

I want review this book

### Contents

SUPPLEMENT TO APPENDIX 3 | 3 |

Problems | 6 |

SUPPLEMENT TO CHAPTER | 18 |

Copyright | |

52 other sections not shown

### Other editions - View all

Introduction to Operations Research Frederick S. Hillier,Gerald J. Lieberman No preview available - 2001 |

### Common terms and phrases

activity additional algorithm allocation allowable amount apply assignment basic solution basic variable BF solution bound boundary called changes coefficients column complete Consider constraint Construct corresponding cost CPF solution decision variables demand described determine distribution dual problem entering equal equations estimates example feasible feasible region feasible solutions FIGURE final flow formulation functional constraints given gives goal identify illustrate increase indicates initial iteration linear programming Maximize million Minimize month needed node nonbasic variables objective function obtained operations optimal optimal solution original parameters path plant possible presented primal problem Prob procedure profit programming problem provides range remaining resource respective resulting revised shown shows side simplex method simplex tableau slack solve step supply Table tableau tion unit values weeks Wyndor Glass zero