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 89
Page 94
... maximize Z -x1 + x2 , does the model have an optimal solution ? If so , find it . If not , explain why not . ( c ) Repeat part ( b ) when the objective is to maximize Z X1 X2 . ( d ) For objective functions where this model has no ...
... maximize Z -x1 + x2 , does the model have an optimal solution ? If so , find it . If not , explain why not . ( c ) Repeat part ( b ) when the objective is to maximize Z X1 X2 . ( d ) For objective functions where this model has no ...
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 343
... Z * ( 0 ) function shown in Fig . 7.1 for para- metric linear programming with systematic changes in the c , pa ... Maximize Z = 2x1 + x2 , subject to X1 X1 ≤10 X2 ≤10 and X2 ≥ 0 . ( a ) Solve this problem graphically . ( b ) Use the ...
... Z * ( 0 ) function shown in Fig . 7.1 for para- metric linear programming with systematic changes in the c , pa ... Maximize Z = 2x1 + x2 , subject to X1 X1 ≤10 X2 ≤10 and X2 ≥ 0 . ( a ) Solve this problem graphically . ( b ) Use the ...
Other editions - View all
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 described 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 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 simplex method slack variables solve the model Solver spreadsheet step subproblem surplus variables Table tion unit profit values weeks Wyndor Glass x₁ zero