## 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 37

X2 A Z = 18 = 3x + 2x2 Maximize Z = 3x1 + 2x2 , subject to X1 < 4 2x2 < 12 3x + 2x2 = 18 and X120 , 8 x220 6 4 Every point on this darker line segment is

X2 A Z = 18 = 3x + 2x2 Maximize Z = 3x1 + 2x2 , subject to X1 < 4 2x2 < 12 3x + 2x2 = 18 and X120 , 8 x220 6 4 Every point on this darker line segment is

**optimal**, each with Z = 18 . Feasible region 2 FIGURE 3.5 The Wyndor Glass Co.Page 130

The model probably has been misformulated , either by omitting relevant constraints or by stating them incorrectly . Alternatively , a computational mistake may have occurred . Multiple

The model probably has been misformulated , either by omitting relevant constraints or by stating them incorrectly . Alternatively , a computational mistake may have occurred . Multiple

**Optimal**Solutions We mentioned in Sec .Page 278

With the current value of C2 = 3 , the

With the current value of C2 = 3 , the

**optimal**solution is ( 4 , Ž ) . When c2 is increased , this solution remains**optimal**only for c2 < 4. For c2 2 4 , ( 0 , 2 ) becomes**optimal**( with a tie at c2 = 4 ) , because of the 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

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### 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