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 102
... solve the model . c ( d ) Use LINGO to formulate this model in a compact form . Then use the LINGO solver to solve the model . c 3.7-2 . Reconsider Prob . 3.1-11 . ( a ) Use MPL / CPLEX to formulate and solve the model for this problem ...
... solve the model . c ( d ) Use LINGO to formulate this model in a compact form . Then use the LINGO solver to solve the model . c 3.7-2 . Reconsider Prob . 3.1-11 . ( a ) Use MPL / CPLEX to formulate and solve the model for this problem ...
Page 178
... solve the problem . 4.6-2 . Consider the following problem . Z = 4x1 + 2x2 + 3x3 + 5x4 , initial ( artificial ) BF solution . Also identify the initial entering basic variable and the leaving basic variable . I ( c ) Work through the ...
... solve the problem . 4.6-2 . Consider the following problem . Z = 4x1 + 2x2 + 3x3 + 5x4 , initial ( artificial ) BF solution . Also identify the initial entering basic variable and the leaving basic variable . I ( c ) Work through the ...
Page 574
... solve this problem . 11.3-19 . Consider the following nonlinear programming problem . Maximize z = x2x2 , subject to x2 + x2 ≤ 2 . ( There are no nonnegativity constraints . ) Use dynamic program- ming to solve this problem . 11.3-20 ...
... solve this problem . 11.3-19 . Consider the following nonlinear programming problem . Maximize z = x2x2 , subject to x2 + x2 ≤ 2 . ( There are no nonnegativity constraints . ) Use dynamic program- ming to solve this problem . 11.3-20 ...
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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