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 37
... optimal solutions if the objective function were changed to Z = 3x1 + 2x2 . Z = 18 = 3x + 2x2 x2 Maximize Z = 3x1 + 2x2 , subject to X1 ≤4 2x2 ... feasible solution is a solution for which all the. 3.2 THE LINEAR PROGRAMMING MODEL 35.
... optimal solutions if the objective function were changed to Z = 3x1 + 2x2 . Z = 18 = 3x + 2x2 x2 Maximize Z = 3x1 + 2x2 , subject to X1 ≤4 2x2 ... feasible solution is a solution for which all the. 3.2 THE LINEAR PROGRAMMING MODEL 35.
Page 38
... optimal solutions if the objective function were changed to Z = 3x1 + 2x2 . Z = 18 = 3x + 2x2 x2 Maximize Z = 3x1 + ... solution that plays the key role when the simplex method searches for an optimal solution . A corner - point ...
... optimal solutions if the objective function were changed to Z = 3x1 + 2x2 . Z = 18 = 3x + 2x2 x2 Maximize Z = 3x1 + ... solution that plays the key role when the simplex method searches for an optimal solution . A corner - point ...
Page 223
... feasible solution is optimal , it must be a CPF solution . ( b ) The number of CPF solutions is at least ( m + n ) ! m ! n ! ( c ) If a CPF solution has adjacent CPF solutions that are better ( as measured by Z ) , then one of these ...
... feasible solution is optimal , it must be a CPF solution . ( b ) The number of CPF solutions is at least ( m + n ) ! m ! n ! ( c ) If a CPF solution has adjacent CPF solutions that are better ( as measured by Z ) , then one of these ...
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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 allowable range artificial variables b₂ basic solution c₁ c₂ changes coefficients column Consider the following cost Courseware CPLEX decision variables dual problem dual simplex method dynamic programming entering basic variable estimates example feasible region feasible solutions final simplex tableau final tableau following problem formulation functional constraints Gaussian elimination given goal programming graphical identify increase initial BF solution integer iteration leaving basic variable linear programming model linear programming problem LINGO LP relaxation lution Maximize Z maximum flow problem Minimize needed node nonbasic variables nonnegativity constraints objective function obtained optimal solution optimality test parameters path plant presented in Sec primal problem Prob procedure range to stay resource right-hand sides sensitivity analysis shadow prices shown slack variables solve the model Solver spreadsheet step subproblem surplus variables Table tion unit profit values weeks Wyndor Glass x₁ zero