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 159
... side for the optimal solution . The next two columns give the shadow price and the current value of the right - hand side ( b ; ) for each constraint . When just one b¡ value is then changed , the last two columns give the allowable ...
... side for the optimal solution . The next two columns give the shadow price and the current value of the right - hand side ( b ; ) for each constraint . When just one b¡ value is then changed , the last two columns give the allowable ...
Page 262
... side column . Consequently , the tableau still will be in proper form from Gaussian elimination and all the nonbasic variable coefficients in row O still will be nonnegative . Therefore , both the conversion to proper form from Gaussian ...
... side column . Consequently , the tableau still will be in proper form from Gaussian elimination and all the nonbasic variable coefficients in row O still will be nonnegative . Therefore , both the conversion to proper form from Gaussian ...
Page 743
... side that gives the most from its final figure . If neither side changes its final figure , or if they both give in the same amount , then the arbitrator normally compromises halfway between ( $ 1.35 in this case ) . Each side now needs ...
... side that gives the most from its final figure . If neither side changes its final figure , or if they both give in the same amount , then the arbitrator normally compromises halfway between ( $ 1.35 in this case ) . Each side now needs ...
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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