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 452
... described in Sec . 9.3 to solve this shortest- path problem . c ( c ) Formulate and solve a spreadsheet model for this problem . 9.3-6 . The Quick Company has learned that a competitor is plan- ning to come out with a new kind of ...
... described in Sec . 9.3 to solve this shortest- path problem . c ( c ) Formulate and solve a spreadsheet model for this problem . 9.3-6 . The Quick Company has learned that a competitor is plan- ning to come out with a new kind of ...
Page 740
... described in Secs . 6.1 and 6.4 . This fact has several important implications . One implication is that the optimal mixed strate- gies for both players can be found by solving only one of the linear programming problems because the ...
... described in Secs . 6.1 and 6.4 . This fact has several important implications . One implication is that the optimal mixed strate- gies for both players can be found by solving only one of the linear programming problems because the ...
Page 838
... described in Sec . 17.4 , D = degenerate distribution ( constant times ) , as discussed in Sec . 17.7 , Ek = G = Erlang distribution ( shape parameter = k ) , as described in Sec . 17.7 , general distribution ( any arbitrary ...
... described in Sec . 17.4 , D = degenerate distribution ( constant times ) , as discussed in Sec . 17.7 , Ek = G = Erlang distribution ( shape parameter = k ) , as described in Sec . 17.7 , general distribution ( any arbitrary ...
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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