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 166
... iteration and the number of iterations . Our next comparisons concern these factors . Interior - point algorithms are far more complicated than the simplex method . Con- siderably more extensive computations are required for each iteration ...
... iteration and the number of iterations . Our next comparisons concern these factors . Interior - point algorithms are far more complicated than the simplex method . Con- siderably more extensive computations are required for each iteration ...
Page 330
... iteration 3 . = Since there is little to be learned by repeating these calculations for additional itera- tions , we shall stop here . However , we do show in Fig . 7.7 the reconfigured feasible re- gion after rescaling based on the ...
... iteration 3 . = Since there is little to be learned by repeating these calculations for additional itera- tions , we shall stop here . However , we do show in Fig . 7.7 the reconfigured feasible re- gion after rescaling based on the ...
Page 371
... iteration , after the difference for every row and column remaining un- der consideration is calculated and displayed , the largest difference is circled and the small- est unit cost in its row or column is enclosed in a box . The ...
... iteration , after the difference for every row and column remaining un- der consideration is calculated and displayed , the largest difference is circled and the small- est unit cost in its row or column is enclosed in a box . The ...
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 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