## 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 99

Page 116

For example , augmenting the solution ( 3 , 2 ) in the example yields the augmented solu- tion ( 3 , 2 , 1 , 8 , 5 ) because the corresponding values of the slack variables are x3 = 1 , X4 = 8 , and x5 5 . = A

For example , augmenting the solution ( 3 , 2 ) in the example yields the augmented solu- tion ( 3 , 2 , 1 , 8 , 5 ) because the corresponding values of the slack variables are x3 = 1 , X4 = 8 , and x5 5 . = A

**basic solution**is an ...Page 243

A key insight here is that the dual

A key insight here is that the dual

**solution**read from row 0 must also be a**basic**so- lution ! The reason is that the m**basic**variables for the primal problem are required to have a coefficient of zero in row 0 , which thereby requires ...Page 245

solved directly to obtain this complementary solution . For example , consider the next - to- last primal

solved directly to obtain this complementary solution . For example , consider the next - to- last primal

**basic solution**in Table 6.9 , ( 4 , 6 , 0 , 0 , −6 ) . Note that x1 , x2 , and x5 are basic variables , since these variables are ...### What people are saying - Write a review

Reviews aren't verified, but Google checks for and removes fake content when it's identified

User Review - Flag as inappropriate

i

User Review - Flag as inappropriate

I want review this book

### 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 allowable amount apply assigned basic solution basic variable BF solution bound boundary called changes coefficients column complete Consider Construct corresponding cost CPF solution decision variables described determine developed dual problem entering 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 linear programming model Maximize million Minimize month needed node objective function obtained operations optimal optimal solution original parameters path perform plant possible presented primal problem Prob procedure profit programming problem provides range resource respective resulting revised sensitivity analysis shown shows side simplex method simplex tableau slack solve step Table tableau tion unit values weeks Wyndor Glass x₁ zero