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 177
... objective function for which this model has no optimal solution . Then work through the simplex method step by step to demonstrate that Z is unbounded . € ( f ) For the objective function selected in part ( e ) , use a software package ...
... objective function for which this model has no optimal solution . Then work through the simplex method step by step to demonstrate that Z is unbounded . € ( f ) For the objective function selected in part ( e ) , use a software package ...
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 596
... objective function . ) The last functional constraint ensures that x1 + x2 + x3 = 5. The linear objective function then gives the total profit according to Table 12.3 . Solving this BIP model gives an optimal solution of = = 0 , Уп Y21 ...
... objective function . ) The last functional constraint ensures that x1 + x2 + x3 = 5. The linear objective function then gives the total profit according to Table 12.3 . Solving this BIP model gives an optimal solution of = = 0 , Уп Y21 ...
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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 Courseware CPLEX decision variables described 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 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 simplex method slack variables solve the model Solver spreadsheet step subproblem surplus variables Table tion unit profit values weeks Wyndor Glass x₁ zero