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 319
... bound technique uses the following rule to make this choice : Rule : Begin with choice 1 . = Whenever x ; O , use choice 1 , so x ; is nonbasic . Whenever x ; = uj , use choice 2 , so yj = O is nonbasic . Switch choices only when the ...
... bound technique uses the following rule to make this choice : Rule : Begin with choice 1 . = Whenever x ; O , use choice 1 , so x ; is nonbasic . Whenever x ; = uj , use choice 2 , so yj = O is nonbasic . Switch choices only when the ...
Page 444
... bound ( 0 ) or its upper bound ( u1 ; ) . For those arcs whose flow increases with 0 in Fig . 9.18 ( arcs A → C and C → E ) , only the upper bounds ( UAC ∞ and uCE = 80 ) need to be considered : = XAC 0≤0 . XCE = = 50 + 0≤ 80 , SO ...
... bound ( 0 ) or its upper bound ( u1 ; ) . For those arcs whose flow increases with 0 in Fig . 9.18 ( arcs A → C and C → E ) , only the upper bounds ( UAC ∞ and uCE = 80 ) need to be considered : = XAC 0≤0 . XCE = = 50 + 0≤ 80 , SO ...
Page 607
... Bound for subproblem 1 : Bound for subproblem 2 : Z≤9 , Z ≤ 16 . Figure 12.5 summarizes these results , where the numbers given just below the nodes are the bounds and below each bound is the optimal solution obtained for the LP ...
... Bound for subproblem 1 : Bound for subproblem 2 : Z≤9 , Z ≤ 16 . Figure 12.5 summarizes these results , where the numbers given just below the nodes are the bounds and below each bound is the optimal solution obtained for the LP ...
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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