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 294
... maximum supply by 1. Use this shadow price to determine the maximum premium that the company should be willing to pay for each subassembly of this type . c ( d ) Repeat part ( c ) for the subassembly B constraint . c ( e ) Estimate how ...
... maximum supply by 1. Use this shadow price to determine the maximum premium that the company should be willing to pay for each subassembly of this type . c ( d ) Repeat part ( c ) for the subassembly B constraint . c ( e ) Estimate how ...
Page 427
... maximum flow problem . FIGURE 9.10 A minimum cut for the Seervada Park maximum flow problem . 2 A 0 7 T 3 2 0 7 3 6 B D 2 5 2 C E 2 2 every directed path from the source to the sink . There normally are many ways to slice through a ...
... maximum flow problem . FIGURE 9.10 A minimum cut for the Seervada Park maximum flow problem . 2 A 0 7 T 3 2 0 7 3 6 B D 2 5 2 C E 2 2 every directed path from the source to the sink . There normally are many ways to slice through a ...
Page 428
... Maximum Flow Problems Most maximum flow problems that arise in practice are considerably larger , and occa- sionally vastly larger , than the Seervada Park problem . Some problems have thousands of nodes and arcs . The augmenting path ...
... Maximum Flow Problems Most maximum flow problems that arise in practice are considerably larger , and occa- sionally vastly larger , than the Seervada Park problem . Some problems have thousands of nodes and arcs . The augmenting path ...
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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