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 69
... shown in Fig . 3.14 includes all these components . The parame- ters for the functional constraints are in rows 5 , 6 , and 7 , and the coefficients for the ob- jective function are in row 8. The values of the decision variables are in ...
... shown in Fig . 3.14 includes all these components . The parame- ters for the functional constraints are in rows 5 , 6 , and 7 , and the coefficients for the ob- jective function are in row 8. The values of the decision variables are in ...
Page 243
... shown in Table 6.7 ) , the correspondence between basic solutions in the primal and dual problems is a symmetric one . Furthermore , a pair of complementary ba- sic solutions has the same objective function value , shown as W in Table ...
... shown in Table 6.7 ) , the correspondence between basic solutions in the primal and dual problems is a symmetric one . Furthermore , a pair of complementary ba- sic solutions has the same objective function value , shown as W in Table ...
Page 283
... shown in Fig . 6.3 , the geometric interpretation of changing the objective function from Z = 3x1 + 5x2 to Z ( 0 ) = ( 3 + 0 ) x1 + ( 520 ) x2 is that we are changing the slope of the original objective function line ( Z = 45 = 3x1 + ...
... shown in Fig . 6.3 , the geometric interpretation of changing the objective function from Z = 3x1 + 5x2 to Z ( 0 ) = ( 3 + 0 ) x1 + ( 520 ) x2 is that we are changing the slope of the original objective function line ( Z = 45 = 3x1 + ...
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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