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
In reality , the duration of each activity is a random variable having some probability distribution . The original version of PERT took this uncertainty into account by using three different types of estimates of the duration of an ...
In reality , the duration of each activity is a random variable having some probability distribution . The original version of PERT took this uncertainty into account by using three different types of estimates of the duration of an ...
Page 879
a case except k = 1 ( exponential distribution ) , which has o = 1 / j . To illustrate a typical sit1 u a uation where o > 1/4 can occur , we suppose that the service involved in the queueing system is the repair of some kind of machine ...
a case except k = 1 ( exponential distribution ) , which has o = 1 / j . To illustrate a typical sit1 u a uation where o > 1/4 can occur , we suppose that the service involved in the queueing system is the repair of some kind of machine ...
Page 1146
( b ) Now do this by using the table for the normal distribution given in Appendix 5 and applying the inverse transformation method . R 22.4-15 . Obtaining uniform random numbers as instructed at the beginning of the Problems section ...
( b ) Now do this by using the table for the normal distribution given in Appendix 5 and applying the inverse transformation method . R 22.4-15 . Obtaining uniform random numbers as instructed at the beginning of the Problems section ...
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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 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