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. |
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Page 158
... Allowable Allowable Cost Coefficient Increase Decrease $ C $ 9 Solution Doors 2 0 3 $ D $ 9 Solution Windows 6 0 5 ... range to stay optimal for each coefficient c ; in the objective function . For any cj , its allowable range to ...
... Allowable Allowable Cost Coefficient Increase Decrease $ C $ 9 Solution Doors 2 0 3 $ D $ 9 Solution Windows 6 0 5 ... range to stay optimal for each coefficient c ; in the objective function . For any cj , its allowable range to ...
Page 266
... range of values for b2 is referred to as its allowable range to stay feasible . For any b1 , recall from Sec . 4.7 that its allowable range to stay feasible is the range of values over which the current optimal BF solution ' ( with ...
... range of values for b2 is referred to as its allowable range to stay feasible . For any b1 , recall from Sec . 4.7 that its allowable range to stay feasible is the range of values over which the current optimal BF solution ' ( with ...
Page 272
... allowable range . Figure 6.3 provides graphical insight into why c1 ≤71⁄2 is the allowable range . At c1 = 72 , the objective function becomes Z = 7.5x1 + 5x2 = 2.5 ( 3x1 + 2x2 ) , so the opti- mal objective line will lie on top of the ...
... allowable range . Figure 6.3 provides graphical insight into why c1 ≤71⁄2 is the allowable range . At c1 = 72 , the objective function becomes Z = 7.5x1 + 5x2 = 2.5 ( 3x1 + 2x2 ) , so the opti- mal objective line will lie on top of the ...
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