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. |
From inside the book
Results 1-3 of 72
Page 298
Consider the following four cases where the true values of b , and b2 differ from their estimates by the same percentage : ( 1 ) both b , and b2 are smaller than their estimates , ( 2 ) both b , and b2 are larger than their estimates ...
Consider the following four cases where the true values of b , and b2 differ from their estimates by the same percentage : ( 1 ) both b , and b2 are smaller than their estimates , ( 2 ) both b , and b2 are larger than their estimates ...
Page 487
Similarly , an approximate formula for u is M = 0 + 4m + P р 6 Intuitively , this formula is placing most of the weight on the most likely estimate and then small equal weights on the other two estimates . MS Project provides the option ...
Similarly , an approximate formula for u is M = 0 + 4m + P р 6 Intuitively , this formula is placing most of the weight on the most likely estimate and then small equal weights on the other two estimates . MS Project provides the option ...
Page 517
Using the PERT three - estimate approach , the three estimates for one of the activities are as follows : optimistic estimate = 30 days , most likely estimate 36 days , pessimistic estimate = 48 days . What are the resulting estimates ...
Using the PERT three - estimate approach , the three estimates for one of the activities are as follows : optimistic estimate = 30 days , most likely estimate 36 days , pessimistic estimate = 48 days . What are the resulting estimates ...
What people are saying - Write a review
We haven't found any reviews in the usual places.
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 allocation allowable range artificial variables assignment problem augmenting path basic solution Big M method changes coefficients column Consider the following constraint boundary corresponding CPLEX decision variables 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 graphically identify increase initial BF solution integer interior-point iteration leaving basic variable linear programming model linear programming problem LP relaxation lution Maximize Maximize Z maximum flow problem Minimize needed node nonbasic variables objective function obtained optimal solution optimality test path Plant presented in Sec primal problem Prob procedure range to stay resource right-hand sides sensitivity analysis shadow prices slack variables solve this model Solver spreadsheet step subproblem surplus variables tion transportation problem transportation simplex method weeks Wyndor Glass x₁ zero