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 176
Maximize Z = 2x1 + 3x2 , subject to xj + 2x2 ≤ 30 x1 + x2 ≤ 20 and x1 = 0 . 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 ...
Maximize Z = 2x1 + 3x2 , subject to xj + 2x2 ≤ 30 x1 + x2 ≤ 20 and x1 = 0 . 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 ...
Page 573
Parallel Units Component 1 123 0.5 0.6 2 3 0.6 0.8 0.7 0.7 0.8 0.5 Maximize subject to Z = x1x3x3 , 0.7 0.8 0.9 0.9 * ≥ 1 , X1 + 2x2 + 3x3 ≤ 10 X2 ≥ 1 , X3 ≥ 1 , The probability that the system will function is the product of the ...
Parallel Units Component 1 123 0.5 0.6 2 3 0.6 0.8 0.7 0.7 0.8 0.5 Maximize subject to Z = x1x3x3 , 0.7 0.8 0.9 0.9 * ≥ 1 , X1 + 2x2 + 3x3 ≤ 10 X2 ≥ 1 , X3 ≥ 1 , The probability that the system will function is the product of the ...
Page 574
Maximize Z = and X2 ≥ 0 . Use dynamic programming to solve this problem . 11.3-23 . Consider the following nonlinear programming problem . Z = 5x1 + x2 , Maximize subject to 2x2 + x2 ≤ 13 x2 + x2 ≤ 9 subject to x2 + x2 ≤ 2 .
Maximize Z = and X2 ≥ 0 . Use dynamic programming to solve this problem . 11.3-23 . Consider the following nonlinear programming problem . Z = 5x1 + x2 , Maximize subject to 2x2 + x2 ≤ 13 x2 + x2 ≤ 9 subject to x2 + x2 ≤ 2 .
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Introduction to Operations Research Frederick S. Hillier,Gerald J. Lieberman No preview available - 2001 |
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activity additional algorithm allowable amount apply assigned basic solution basic variable BF solution bound boundary called changes coefficients column complete Consider Construct corresponding cost CPF solution decision variables described determine developed dual problem entering 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 linear programming model Maximize million Minimize month needed node objective function obtained operations optimal optimal solution original parameters path perform plant possible presented primal problem Prob procedure profit programming problem provides range resource respective resulting revised sensitivity analysis shown shows side simplex method simplex tableau slack solve step Table tableau tion unit values weeks Wyndor Glass x₁ zero