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 59
... constraint because the seventh constraint is x3 + x4 ≥ 82. ( In fact , three of the nonnegativity constraints - x1 ≥ 0 , X40 , x50 - also are redundant constraints because of the first , eighth , and tenth func- tional constraints ...
... constraint because the seventh constraint is x3 + x4 ≥ 82. ( In fact , three of the nonnegativity constraints - x1 ≥ 0 , X40 , x50 - also are redundant constraints because of the first , eighth , and tenth func- tional constraints ...
Page 586
Frederick S. Hillier, Gerald J. Lieberman. Either - Or Constraints Consider the important case where a choice can be made between two constraints , so that only one ( either one ) must hold ( whereas the other one can hold but is not ...
Frederick S. Hillier, Gerald J. Lieberman. Either - Or Constraints Consider the important case where a choice can be made between two constraints , so that only one ( either one ) must hold ( whereas the other one can hold but is not ...
Page 587
... constraints such that only some K of these constraints must hold . ( Assume that K < N. ) Part of the opti- mization process is to choose the combination of K constraints that permits the objective function to reach its best possible ...
... constraints such that only some K of these constraints must hold . ( Assume that K < N. ) Part of the opti- mization process is to choose the combination of K constraints that permits the objective function to reach its best possible ...
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 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