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 237
If x is not optimal for the primal problem , then y is not feasible for the dual problem . = 2 To illustrate , after one iteration for the Wyndor Glass Co. problem , xy = 0 , x2 = 6 , and yı = 0 , yz = { , yz = 0 , with cx = 30 = yb .
If x is not optimal for the primal problem , then y is not feasible for the dual problem . = 2 To illustrate , after one iteration for the Wyndor Glass Co. problem , xy = 0 , x2 = 6 , and yı = 0 , yz = { , yz = 0 , with cx = 30 = yb .
Page 286
Construct and graph a primal problem with two decision variables and two functional constraints that has feasible solutions and an unbounded objective function . Then construct the dual problem and demonstrate graphically that it has no ...
Construct and graph a primal problem with two decision variables and two functional constraints that has feasible solutions and an unbounded objective function . Then construct the dual problem and demonstrate graphically that it has no ...
Page 287
( b ) At each iteration , the simplex method simultaneously identifies a CPF solution for the primal problem and a CPF solution for the dual problem such that their objective function values are the same . ( c ) If the primal problem ...
( b ) At each iteration , the simplex method simultaneously identifies a CPF solution for the primal problem and a CPF solution for the dual problem such that their objective function values are the same . ( c ) If the primal problem ...
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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 allocation allowable range artificial variables assignment problem augmenting path basic solution Big M method changes coefficients column Consider the following constraint boundary corresponding CPLEX decision variables dual problem dynamic programming entering basic variable example feasible region feasible solutions final simplex tableau final tableau following problem formulation functional constraints Gaussian elimination given goal goal programming graphically identify increase initial BF solution integer interior-point iteration leaving basic variable linear programming model linear programming problem LP relaxation lution Maximize Maximize Z maximum flow problem Minimize needed node nonbasic variables objective function obtained optimal solution optimality test path Plant presented in Sec primal problem Prob procedure range to stay resource right-hand sides sensitivity analysis shadow prices slack variables solve this model Solver spreadsheet step subproblem surplus variables tion transportation problem transportation simplex method weeks Wyndor Glass x₁ zero