## 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 82

Page 130

Multiple

fact was illustrated in Fig. 3.5 by changing the objective function in the Wyndor

Glass ...

Multiple

**Optimal**Solutions We mentioned in Sec. 3.2 (under the definition of**optimal**solution) that a problem can have more than one**optimal**solution. Thisfact was illustrated in Fig. 3.5 by changing the objective function in the Wyndor

Glass ...

Page 278

When c2 is increased, this solution remains

, j) becomes

+ Ax2 = 18. When c2 is decreased instead, (4, l) remains

When c2 is increased, this solution remains

**optimal**only for c2 — 4. For c2 ^ 4, (0, j) becomes

**optimal**(with a tie at c2 = 4), because of the constraint boundary 3jci+ Ax2 = 18. When c2 is decreased instead, (4, l) remains

**optimal**only for c2 > 0.Page 288

Then draw your conclusions about whether these two basic solutions are

for their respective problems, l (d) Solve the dual problem graphically. Use this

solution to identify the basic variables and the nonbasic variables for the

...

Then draw your conclusions about whether these two basic solutions are

**optimal**for their respective problems, l (d) Solve the dual problem graphically. Use this

solution to identify the basic variables and the nonbasic variables for the

**optimal**...

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### Contents

SUPPLEMENT TO APPENDIX 3 | 3 |

Problems | 6 |

An Algorithm for the Assignment Problem | 18 |

Copyright | |

44 other sections not shown

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activity additional algorithm alternative amount analysis apply assigned assumed basic variable begin BF solution bound calculate called changes coefficients column complete Consider constraints Construct corresponding cost CPF solution customers decision demand described determine developed distribution entering equations estimated example expected feasible FIGURE final flow formulation given gives hour identify illustrate increase indicates initial inventory iteration linear programming machine Maximize maximum mean million Minimize month needed node objective function obtained operations optimal optimal solution original parameter path payoff perform plant player possible presented Prob probability problem procedure profit programming problem queueing respectively resulting shown shows side simplex method solution solve step strategy Table tableau tion transportation unit weeks