## 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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Page 191

x1 = 0 ( 0,9 ) Maximize Z = 3x1 + 5x2 , subject to Xi < 4 2x2 = 12 2xy + 3x2 = 18 and x 20 , X2 20 3x1 + 2x2 = 18 = ( 2,6 ) ( 4,6 ) ( 0,6 ) 2x2 = = 12 x1 = 4 Feasible region ( 4,3 ) FIGURE 5.1 Constraint

x1 = 0 ( 0,9 ) Maximize Z = 3x1 + 5x2 , subject to Xi < 4 2x2 = 12 2xy + 3x2 = 18 and x 20 , X2 20 3x1 + 2x2 = 18 = ( 2,6 ) ( 4,6 ) ( 0,6 ) 2x2 = = 12 x1 = 4 Feasible region ( 4,3 ) FIGURE 5.1 Constraint

**boundaries**, constraint**boundary**...Page 195

on one additional constraint

on one additional constraint

**boundary**( so that these endpoints are CPF solutions ) . ... When you shift from a geometric viewpoint to an algebraic one , intersection of con- straint**boundaries**changes to simultaneous solution of ...Page 199

Recall that each corner - point solution is the simultaneous solution of a system of n constraint

Recall that each corner - point solution is the simultaneous solution of a system of n constraint

**boundary**equations , which we called its defining equations . The key ques- tion is : How do we tell whether a particular constraint ...### 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 algebraic algorithm allocation allowable range artificial variables assignment problem augmenting path basic solution Big M method changes coefficients column Consider the following constraint boundary corresponding CPLEX decision variables dual problem dynamic programming entering basic variable example feasible region feasible solutions final simplex tableau final tableau following problem formulation functional constraints Gaussian elimination given goal goal programming graphically identify increase initial BF solution integer interior-point iteration leaving basic variable linear programming model linear programming problem LP relaxation lution Maximize Maximize Z maximum flow problem Minimize needed node nonbasic variables objective function obtained optimal solution optimality test path Plant presented in Sec primal problem Prob procedure range to stay resource right-hand sides sensitivity analysis shadow prices slack variables solve this model Solver spreadsheet step subproblem surplus variables tion transportation problem transportation simplex method weeks Wyndor Glass x₁ zero