## 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. |

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Page 119

Choose X1 and x2 to be the

0 , 0 , 4 , 12 , 18 ) . Not optimal , because increasing either

or x2 ) increases Z. Optimality test Iteration 1 Not optimal , because moving ...

Choose X1 and x2 to be the

**nonbasic variables**( = 0 ) for the initial BF solution : (0 , 0 , 4 , 12 , 18 ) . Not optimal , because increasing either

**nonbasic variable**( X1or x2 ) increases Z. Optimality test Iteration 1 Not optimal , because moving ...

Page 199

Each such indicating variable is called a

basic solution . The resulting conclusions and terminology ( already introduced in

Sec . 4.2 ) are summarized next . Each basic solution has m basic variables ...

Each such indicating variable is called a

**nonbasic variable**for the correspondingbasic solution . The resulting conclusions and terminology ( already introduced in

Sec . 4.2 ) are summarized next . Each basic solution has m basic variables ...

Page 200

A BF solution is a basic solution where all m basic variables are nonnegative ( 0 )

. A BF solution is said to be degenerate if any of these m variables equals zero .

Thus , it is possible for a variable to be zero and still not be a

A BF solution is a basic solution where all m basic variables are nonnegative ( 0 )

. A BF solution is said to be degenerate if any of these m variables equals zero .

Thus , it is possible for a variable to be zero and still not be a

**nonbasic variable**...### What people are saying - Write a review

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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