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 118
... presented in Sec . 4.1 ) is based on the original form of the model ( no slack variables ) , so again refer to Fig . 4.1 for a visualization when you examine the second col- umn of the table . Refer to the augmented form of the model ...
... presented in Sec . 4.1 ) is based on the original form of the model ( no slack variables ) , so again refer to Fig . 4.1 for a visualization when you examine the second col- umn of the table . Refer to the augmented form of the model ...
Page 227
... presented in Sec . 5.3 to identify the missing numbers in the current simplex tableau . Show your calculations . ( b ) ... presented in Sec . 5.3 to identify the value of ( c1 , C2 , C3 ) that was used . ( b ) Use the fundamental insight ...
... presented in Sec . 5.3 to identify the missing numbers in the current simplex tableau . Show your calculations . ( b ) ... presented in Sec . 5.3 to identify the value of ( c1 , C2 , C3 ) that was used . ( b ) Use the fundamental insight ...
Page 908
... described in Sec . 17.1 . ) With one slight exception , this system fits the finite calling population variation of the M / M / S model presented in Sec . 17.6 , where N = 10 machines , A = customer per day ( for each operating machine ) ...
... described in Sec . 17.1 . ) With one slight exception , this system fits the finite calling population variation of the M / M / S model presented in Sec . 17.6 , where N = 10 machines , A = customer per day ( for each operating machine ) ...
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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 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