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 143
... method ( with the Big M method ) for the artificial problem that corresponds to the real problem of Fig . 4.5 . X2 ( 0 , 12 ) Z = 6 + 1.2M Constraints for the artificial problem : 0.3x1 + 0.1x22.7 0.5x1 + 0.5x26 ( = holds when x4 = 0 ) ...
... method ( with the Big M method ) for the artificial problem that corresponds to the real problem of Fig . 4.5 . X2 ( 0 , 12 ) Z = 6 + 1.2M Constraints for the artificial problem : 0.3x1 + 0.1x22.7 0.5x1 + 0.5x26 ( = holds when x4 = 0 ) ...
Page 148
... Big M and two - phase methods . Begin with their ob- jective functions . Big M Method : Minimize Z = 0.4x1 + 0.5x2 + MX4 + MÃ6 . Two - Phase Method : Phase 1 : Phase 2 : Minimize Minimize Z = X4 + X6 . Z = 0.4x0.5x2 . Because the M4 and ...
... Big M and two - phase methods . Begin with their ob- jective functions . Big M Method : Minimize Z = 0.4x1 + 0.5x2 + MX4 + MÃ6 . Two - Phase Method : Phase 1 : Phase 2 : Minimize Minimize Z = X4 + X6 . Z = 0.4x0.5x2 . Because the M4 and ...
Page 178
... Big M method , construct the complete first simplex tableau for the simplex method and identify the corresponding initial ( artificial ) BF solution . Also identify the initial entering basic variable and the leaving basic variable . I ...
... Big M method , construct the complete first simplex tableau for the simplex method and identify the corresponding initial ( artificial ) BF solution . Also identify the initial entering basic variable and the leaving basic variable . I ...
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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 corresponding cost Courseware CPF solution CPLEX decision variables 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 interior-point iteration leaving basic variable linear programming model linear programming problem LINGO LP relaxation lution Maximize subject 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 simplex method slack variables solve the model Solver spreadsheet step subproblem surplus variables Table tion values weeks Wyndor Glass x₁ zero