## Introduction to Operations Research, Volume 1CD-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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9 2 = For example , augmenting the solution ( 3 , 2 ) in the example yields the augmented solution ( 3 , 2 , 1 , 8 , 5 ) because the corresponding values of the slack variables are x3 = 1 , X4 = 8 , and X5 5 . A

9 2 = For example , augmenting the solution ( 3 , 2 ) in the example yields the augmented solution ( 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 solution read from row 0 must also be a

A key insight here is that the dual solution read from row 0 must also be a

**basic solution**. 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 - tolast primal

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

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

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Introduction to Operations Research Frederick S. Hillier,Gerald J. Lieberman No preview available - 2001 |

### Common terms and phrases

activity additional algorithm allowable amount apply assignment basic solution basic variable BF solution bound boundary called changes coefficients column complete Consider constraints Construct corresponding cost CPF solution decision variables demand described determine direction distribution dual problem entering equal equations estimates example feasible feasible region 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 shown shows side simplex method simplex tableau slack solve step supply Table tableau tion unit weeks Wyndor Glass zero