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 216
Even when the simplex method has gone through hundreds or thousands of iterations , the coefficients of the slack variables in the final tableau will reveal how this tableau has been obtained from the initial tableau .
Even when the simplex method has gone through hundreds or thousands of iterations , the coefficients of the slack variables in the final tableau will reveal how this tableau has been obtained from the initial tableau .
Page 252
With the Big M method , since M has been added initially to the coefficient of each artificial variable in row 0 , the current value of ... After M is subtracted from the coefficients of the artificial variables ła and to the optimal ...
With the Big M method , since M has been added initially to the coefficient of each artificial variable in row 0 , the current value of ... After M is subtracted from the coefficients of the artificial variables ła and to the optimal ...
Page 273
Analyzing Simultaneous Changes in Objective Function Coefficients . Regardless of whether x ; is a basic or nonbasic variable , the allowable range to stay optimal for c ; is valid only if this objective function coefficient is the only ...
Analyzing Simultaneous Changes in Objective Function Coefficients . Regardless of whether x ; is a basic or nonbasic variable , the allowable range to stay optimal for c ; is valid only if this objective function coefficient is the only ...
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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 allocation allowable range artificial variables assignment problem augmenting path basic solution Big M method changes coefficients column Consider the following constraint boundary corresponding CPLEX decision variables 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 graphically identify increase initial BF solution integer interior-point iteration leaving basic variable linear programming model linear programming problem LP relaxation lution Maximize Maximize Z maximum flow problem Minimize needed node nonbasic variables objective function obtained optimal solution optimality test path Plant presented in Sec primal problem Prob procedure range to stay resource right-hand sides sensitivity analysis shadow prices slack variables solve this model Solver spreadsheet step subproblem surplus variables tion transportation problem transportation simplex method weeks Wyndor Glass x₁ zero