Introduction to Operations ResearchCD-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. |
From inside the book
Results 1-3 of 99
Page 116
... basic solution ( 4 , 6 , 0 , 0 , −6 ) . The fact that corner - point solutions ( and so basic solutions ) can be either feasible or infeasible implies the following definition : A basic feasible ( BF ) solution is an augmented CPF solution ...
... basic solution ( 4 , 6 , 0 , 0 , −6 ) . The fact that corner - point solutions ( and so basic solutions ) can be either feasible or infeasible implies the following definition : A basic feasible ( BF ) solution is an augmented CPF solution ...
Page 243
... solution read from row 0 must also be a basic so- lution ! The reason is that the m basic variables for the primal problem are required to have a coefficient of zero in row 0 , which thereby requires the m associated dual variables to ...
... solution read from row 0 must also be a basic so- lution ! The reason is that the m basic variables for the primal problem are required to have a coefficient of zero in row 0 , which thereby requires the m associated dual variables to ...
Page 245
... solution . For example , consider the next - to- last primal basic solution in Table 6.9 , ( 4 , 6 , 0 , 0 , −6 ) . Note that x1 , x2 , and x5 are basic variables , since these variables are not equal to 0. Table 6.7 indicates that ...
... solution . For example , consider the next - to- last primal basic solution in Table 6.9 , ( 4 , 6 , 0 , 0 , −6 ) . Note that x1 , x2 , and x5 are basic variables , since these variables are not equal to 0. Table 6.7 indicates that ...
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 Courseware CPLEX decision variables dual problem dual simplex method dynamic programming entering basic variable estimates example feasible region feasible solutions final simplex tableau final tableau following problem formulation functional constraints Gaussian elimination given goal programming graphical identify increase initial BF solution integer 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 resource right-hand sides sensitivity analysis shadow prices shown slack variables solve the model Solver spreadsheet step subproblem surplus variables Table tion unit profit values weeks Wyndor Glass x₁ zero