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 286
Consider the following problem . Maximize Z = - -X1 - 2x2 - X3 , subject to and x1 + x2 + 2x3 ≤ 12 - X1 X2 X3 ≤ 1 X2 ≥ 0 , xz ≥ 0 . ( a ) Construct the dual problem . ( b ) Use duality theory to show that the optimal solution for ...
Consider the following problem . Maximize Z = - -X1 - 2x2 - X3 , subject to and x1 + x2 + 2x3 ≤ 12 - X1 X2 X3 ≤ 1 X2 ≥ 0 , xz ≥ 0 . ( a ) Construct the dual problem . ( b ) Use duality theory to show that the optimal solution for ...
Page 288
sic solution for the dual problem by using Eq . ( 0 ) for the pri- mal problem . Then draw your conclusions about whether these two basic solutions are optimal for their respective problems . 1 ( d ) Solve the dual problem graphically .
sic solution for the dual problem by using Eq . ( 0 ) for the pri- mal problem . Then draw your conclusions about whether these two basic solutions are optimal for their respective problems . 1 ( d ) Solve the dual problem graphically .
Page 289
( a ) Construct the dual problem . ( b ) Use graphical analysis of the dual problem to determine whether the primal problem has feasible solutions and , if so , whether its objective function is bounded . 6.4-5 .
( a ) Construct the dual problem . ( b ) Use graphical analysis of the dual problem to determine whether the primal problem has feasible solutions and , if so , whether its objective function is bounded . 6.4-5 .
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Introduction to Operations Research Frederick S. Hillier,Gerald J. Lieberman No preview available - 2001 |
Common terms and phrases
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