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 186
... customers are able to perform transactions on their desktop computers either at home or at work . The explosion of the Internet means that many potential customers understand and use the World Wide Web . He therefore feels that if ...
... customers are able to perform transactions on their desktop computers either at home or at work . The explosion of the Internet means that many potential customers understand and use the World Wide Web . He therefore feels that if ...
Page 891
... customers who are waiting to begin a haircut , so the number of customers in the shop varies between 0 and 4. For n = 0 , 1 , 2 , 3 , 4 , the probability Pn that exactly n customers are in the shop is Po , P 、 P2 = 16 , P3 = 16 , P4 ...
... customers who are waiting to begin a haircut , so the number of customers in the shop varies between 0 and 4. For n = 0 , 1 , 2 , 3 , 4 , the probability Pn that exactly n customers are in the shop is Po , P 、 P2 = 16 , P3 = 16 , P4 ...
Page 904
... customers are more important than type 2 customers . If the queue discipline were changed from first - come- first - served to a priority system with type 1 customers being given nonpreemptive priority over type 2 customers , would this ...
... customers are more important than type 2 customers . If the queue discipline were changed from first - come- first - served to a priority system with type 1 customers being given nonpreemptive priority over type 2 customers , would this ...
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