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 489
... mean critical path is the path through the project network that would be the critical path if the duration of each activity equals its mean . Reliable's mean critical path is START → A → B → C → E → F → J → L → N → FINISH , as ...
... mean critical path is the path through the project network that would be the critical path if the duration of each activity equals its mean . Reliable's mean critical path is START → A → B → C → E → F → J → L → N → FINISH , as ...
Page 491
... mean up = 44 and variance o d = deadline for the project = 47 weeks . = = 9 , Since the standard deviation of T is σ = 3 , the number of standard deviations by which d exceeds Ka Mp is d - Mp = = Op 47 - 44 3 = 1 . Therefore , using ...
... mean up = 44 and variance o d = deadline for the project = 47 weeks . = = 9 , Since the standard deviation of T is σ = 3 , the number of standard deviations by which d exceeds Ka Mp is d - Mp = = Op 47 - 44 3 = 1 . Therefore , using ...
Page 930
... mean service rate of the crew is propor- tional to the square root of its size . What should the size be to minimize expected total cost per hour ? 18.4-14 . Trucks arrive at a warehouse according to a Poisson process with a mean rate ...
... mean service rate of the crew is propor- tional to the square root of its size . What should the size be to minimize expected total cost per hour ? 18.4-14 . Trucks arrive at a warehouse according to a Poisson process with a mean rate ...
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