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 486
... distribution . The original version of PERT took this uncertainty into account by using three dif- ferent types of estimates of the duration of an activity to obtain basic information about its probability distribution , as described ...
... distribution . The original version of PERT took this uncertainty into account by using three dif- ferent types of estimates of the duration of an activity to obtain basic information about its probability distribution , as described ...
Page 873
... distribution assumes a very large variation ( σ = 1 / μ ) . Between these two rather extreme cases lies a long middle ground ( 0 < σ < 1 / μ ) , where most actual ser- vice - time distributions fall . Another kind of theoretical service ...
... distribution assumes a very large variation ( σ = 1 / μ ) . Between these two rather extreme cases lies a long middle ground ( 0 < σ < 1 / μ ) , where most actual ser- vice - time distributions fall . Another kind of theoretical service ...
Page 1146
... distribution given in Appendix 5 and applying the inverse transformation method . 0 R 22.4-12 . Obtaining uniform random numbers as instructed at the beginning of the Problems section , generate four random observa- tions ...
... distribution given in Appendix 5 and applying the inverse transformation method . 0 R 22.4-12 . Obtaining uniform random numbers as instructed at the beginning of the Problems section , generate four random observa- tions ...
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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