## 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. |

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Page 191

x1 = 0 ( 0,9 ) Maximize Z = 3x1 + 5x2 , subject to X1 s 4 2x2 = 12 2xy + 3x2 = 18 and Xi 20 , X220 3x1 + 2x2 = 18 ( 2,6 ) ( 4 , 6 ) ( 0,6 ) 2x2 = 12 x1 = 4 Feasible region ( 4 , 3 ) FIGURE 5.1 Constraint

x1 = 0 ( 0,9 ) Maximize Z = 3x1 + 5x2 , subject to X1 s 4 2x2 = 12 2xy + 3x2 = 18 and Xi 20 , X220 3x1 + 2x2 = 18 ( 2,6 ) ( 4 , 6 ) ( 0,6 ) 2x2 = 12 x1 = 4 Feasible region ( 4 , 3 ) FIGURE 5.1 Constraint

**boundaries**, constraint**boundary**...Page 195

The intersection of this first new constraint

The intersection of this first new constraint

**boundary**with the two constraint**boundaries**forming the edge yields the new CPF solution ( 4 , 2 , 4 ) . When n > 3 , these same concepts generalize to higher dimensions , except the ...Page 199

Recall that each corner - point solution is the simultaneous solution of a system of n constraint

Recall that each corner - point solution is the simultaneous solution of a system of n constraint

**boundary**equations , which we called its defining equations . The key question is : How do we tell whether a particular constraint ...### What people are saying - Write a review

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### 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