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 176
... Maximize Z = 2x1 + 3x2 , subject to and x1 + 2x2 ≤ 30 x1 + x2 ≤ 20 X2 ≥ 0 . 4.4-6 . Consider the following problem . Maximize Z = 2x1 + 4x2 + 3x3 , subject to 3x1 + 4x2 + 2x3 ≤ 60 2x1 + x2 + 2x3 ≤40 x1 + 3x2 + 2x3 ≤ 80 x2 ≥ 0 ...
... Maximize Z = 2x1 + 3x2 , subject to and x1 + 2x2 ≤ 30 x1 + x2 ≤ 20 X2 ≥ 0 . 4.4-6 . Consider the following problem . Maximize Z = 2x1 + 4x2 + 3x3 , subject to 3x1 + 4x2 + 2x3 ≤ 60 2x1 + x2 + 2x3 ≤40 x1 + 3x2 + 2x3 ≤ 80 x2 ≥ 0 ...
Page 573
... Maximize subject to Z = x1x3x3 , 0.7 0.8 0.9 0.9 X1 + 2x2 + 3x3 ≤ 10 X2 ≥ 1 , x3 ≥ 1 , The probability that the system will function is the product of the probabilities that the respective components will function . The cost ( in ...
... Maximize subject to Z = x1x3x3 , 0.7 0.8 0.9 0.9 X1 + 2x2 + 3x3 ≤ 10 X2 ≥ 1 , x3 ≥ 1 , The probability that the system will function is the product of the probabilities that the respective components will function . The cost ( in ...
Page 574
... Maximize Z = x2x2 , subject to x2 + x2 ≤ 2 . ( There are no nonnegativity constraints . ) Use dynamic program- ming to solve this problem . 11.3-20 . Consider the following nonlinear programming problem . Maximize Z = x2 + 4x2 + ...
... Maximize Z = x2x2 , subject to x2 + x2 ≤ 2 . ( There are no nonnegativity constraints . ) Use dynamic program- ming to solve this problem . 11.3-20 . Consider the following nonlinear programming problem . Maximize Z = x2 + 4x2 + ...
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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 corresponding cost Courseware CPF solution CPLEX decision variables dual problem dynamic programming entering basic variable estimates example feasible region feasible solutions final simplex tableau final tableau flow following problem formulation functional constraints Gaussian elimination given graphical identify increase initial BF solution integer interior-point iteration leaving basic variable linear programming model linear programming problem LINGO LP relaxation lution Maximize subject 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 right-hand sides sensitivity analysis shadow prices simplex method slack variables solve the model Solver spreadsheet step subproblem surplus variables Table tion values weeks Wyndor Glass x₁ zero