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 273
... objective function coefficient is the only one being changed . How- ever , when simultaneous changes are made in the coefficients of the objective function , a 100 percent rule is available for checking whether the original solution ...
... objective function coefficient is the only one being changed . How- ever , when simultaneous changes are made in the coefficients of the objective function , a 100 percent rule is available for checking whether the original solution ...
Page 665
... objective is simply to Maximize f ( x ) over all values of x = ( X1 , X2 , x ) . As reviewed in Appendix 3 , the necessary condi- tion that a particular solution x = x * be optimal when f ( x ) is a differentiable function is af axi = 0 ...
... objective is simply to Maximize f ( x ) over all values of x = ( X1 , X2 , x ) . As reviewed in Appendix 3 , the necessary condi- tion that a particular solution x = x * be optimal when f ( x ) is a differentiable function is af axi = 0 ...
Page 698
... objective function ( enabling us to use the simplex method ) with the one - dimensional search procedure of Sec . 13.4 . A Sequential Linear Approximation Algorithm ( Frank - Wolfe ) Given a feasible trial solution x ' , the linear ...
... objective function ( enabling us to use the simplex method ) with the one - dimensional search procedure of Sec . 13.4 . A Sequential Linear Approximation Algorithm ( Frank - Wolfe ) Given a feasible trial solution x ' , the linear ...
Other editions - View all
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 Courseware CPLEX decision variables dual problem dual simplex method dynamic programming entering basic variable estimates example feasible region feasible solutions final simplex tableau final tableau following problem formulation functional constraints Gaussian elimination given goal programming graphical identify increase initial BF solution integer 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 resource right-hand sides sensitivity analysis shadow prices shown slack variables solve the model Solver spreadsheet step subproblem surplus variables Table tion unit profit values weeks Wyndor Glass x₁ zero