## 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 77

Page 166

The two basic factors that determine the performance of an

The two basic factors that determine the performance of an

**algorithm**on a real problem are the average computer time per iteration and the number of iterations . Our next comparisons concern these factors . Interior - point**algorithms**...Page 613

Also included in the OR Courseware is an interactive routine for executing this

Also included in the OR Courseware is an interactive routine for executing this

**algorithm**. As usual , the Excel , LINGO / LINDO , and MPL / CPLEX ... The**algorithms**they use for BIP problems all are similar to the one described above .Page 698

Although these

Although these

**algorithms**are particularly suitable for linearly constrained optimization problems , some also can ... As one example of a sequential - approximation**algorithm**, we present here the FrankWolfe**algorithm**for the case of ...### 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