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
... primal problem , then y is not feasible for the dual problem . 5 - 2 , Y3 = = = To illustrate , after one iteration for the Wyndor Glass Co. problem , x1 = 0 , x2 = 6 , and X2 O , y2 = 0 , with cx 30 yb . This x is feasible for the primal ...
... primal problem , then y is not feasible for the dual problem . 5 - 2 , Y3 = = = To illustrate , after one iteration for the Wyndor Glass Co. problem , x1 = 0 , x2 = 6 , and X2 O , y2 = 0 , with cx 30 yb . This x is feasible for the primal ...
Page 286
... primal problem has an unbounded feasible region that permits increasing Z indefinitely , then the dual problem has no feasible solutions . 6.1-14 . Consider the primal and dual problems in our standard form presented in matrix notation ...
... primal problem has an unbounded feasible region that permits increasing Z indefinitely , then the dual problem has no feasible solutions . 6.1-14 . Consider the primal and dual problems in our standard form presented in matrix notation ...
Page 287
... primal problem cannot exceed 25 . ( c ) It has been conjectured that x2 and x3 should be the basic vari- ables for the optimal solution of the primal problem . Directly derive this basic ... problem by using Eq . CHAPTER 6 PROBLEMS 287.
... primal problem cannot exceed 25 . ( c ) It has been conjectured that x2 and x3 should be the basic vari- ables for the optimal solution of the primal problem . Directly derive this basic ... problem by using Eq . CHAPTER 6 PROBLEMS 287.
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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 cost CPF solution CPLEX decision variables described 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 graphical identify increase initial BF solution integer interior-point 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 right-hand sides sensitivity analysis shadow prices shown simplex method slack variables solve the model Solver spreadsheet step subproblem surplus variables Table tion values weeks Wyndor Glass x₁ zero