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 177
... feasible region is unbounded . ( b ) If the objective is to 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 ...
... feasible region is unbounded . ( b ) If the objective is to 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 ...
Page 198
... feasible region goes down from ( 2 , 6 ) to ( 3 , 5 ) and then " bends outward " to ( 4 , 5 ) , beyond the objective function line passing through ( 2 , 6 ) . The key point is that the kind of situation illustrated in Fig . 5.3 can ...
... feasible region goes down from ( 2 , 6 ) to ( 3 , 5 ) and then " bends outward " to ( 4 , 5 ) , beyond the objective function line passing through ( 2 , 6 ) . The key point is that the kind of situation illustrated in Fig . 5.3 can ...
Page 703
... feasible region . We shall describe the other version , which is an interior - point algorithm that deals directly with feasible solutions while using a barrier function to force staying inside the feasible region . Although SUMT was ...
... feasible region . We shall describe the other version , which is an interior - point algorithm that deals directly with feasible solutions while using a barrier function to force staying inside the feasible region . Although SUMT was ...
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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