## 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 87

Page 148

Begin with their

Mx4 + Mxo . Two - Phase Method : Phase 1 : Phase 2 : Minimize Minimize Z = X4

+ X6 . Z = 0.4x1 + 0.5x2 . Because the Mx4 and Mxo terms dominate the 0.4x ...

Begin with their

**objective functions**. Big M Method : Minimize Z = 0.4x1 + 0.5x2 +Mx4 + Mxo . Two - Phase Method : Phase 1 : Phase 2 : Minimize Minimize Z = X4

+ X6 . Z = 0.4x1 + 0.5x2 . Because the Mx4 and Mxo terms dominate the 0.4x ...

Page 273

Analyzing Simultaneous Changes in

Regardless of whether X ; is a basic or nonbasic variable , the allowable range to

stay optimal for c ; is valid only if this

being ...

Analyzing Simultaneous Changes in

**Objective Function**Coefficients .Regardless of whether X ; is a basic or nonbasic variable , the allowable range to

stay optimal for c ; is valid only if this

**objective function**coefficient is the only onebeing ...

Page 698

This procedure is particularly straightforward ; it combines linear approximations

of the

dimensional search procedure of Sec . 13.4 . A Sequential Linear Approximation

...

This procedure is particularly straightforward ; it combines linear approximations

of the

**objective function**( enabling us to use the simplex method ) with the one -dimensional search procedure of Sec . 13.4 . A Sequential Linear Approximation

...

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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