## 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 75

Page 573

*3 are integers. Use dynamic programming to solve this problem. 11.3-14.*

Consider the following nonlinear

*-. - 3x1, Because of budget limitations, a maximum of $ 1 ,000 can be expended

.

*3 are integers. Use dynamic programming to solve this problem. 11.3-14.*

Consider the following nonlinear

**programming problem**. Z = 36*! + 9*? - 6x\ + 36*-. - 3x1, Because of budget limitations, a maximum of $ 1 ,000 can be expended

.

Page 574

Use dynamic programming to solve this problem. 11.3-19. Consider the following

nonlinear

are no nonnegativity constraints.) Use dynamic programming to solve this ...

Use dynamic programming to solve this problem. 11.3-19. Consider the following

nonlinear

**programming problem**. Maximize Z = x\x2, subject to x\ + x2 ^ 2. (Thereare no nonnegativity constraints.) Use dynamic programming to solve this ...

Page 714

Consider the following quadratic

4x2 — x\, subject to x, + x2 £ 2 and xi > 0, x2 a 0. (a) Use the KKT conditions to

derive an optimal solution. (b) Now suppose that this problem is to be solved by ...

Consider the following quadratic

**programming problem**: Maximize /(x) = &X| - xj +4x2 — x\, subject to x, + x2 £ 2 and xi > 0, x2 a 0. (a) Use the KKT conditions to

derive an optimal solution. (b) Now suppose that this problem is to be solved by ...

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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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### Common terms and phrases

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