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 763
... Expected Payoff with Perfect Information = 242.5 FIGURE 15.8 This Excel template for obtaining the expected payoff with perfect information is applied here to the first Goferbroke Co. problem . C D E F G 11 = MAX ( C5 : C9 ) = MAX ( D5 ...
... Expected Payoff with Perfect Information = 242.5 FIGURE 15.8 This Excel template for obtaining the expected payoff with perfect information is applied here to the first Goferbroke Co. problem . C D E F G 11 = MAX ( C5 : C9 ) = MAX ( D5 ...
Page 764
... expected increase in payoff ( excluding the cost of the experiment ) due to performing ex- perimentation , we now will do somewhat more work to calculate this expected increase directly . This quantity is called the expected value of ...
... expected increase in payoff ( excluding the cost of the experiment ) due to performing ex- perimentation , we now will do somewhat more work to calculate this expected increase directly . This quantity is called the expected value of ...
Page 1203
... Expected average cost per unit time , 1055 , 1077 for complex cost functions , 816-818 in Markov chains , 814-816 Expected interarrival time , 839 Expected monetary value criterion , 754n Expected payoff , decision trees , 767-769 Expected ...
... Expected average cost per unit time , 1055 , 1077 for complex cost functions , 816-818 in Markov chains , 814-816 Expected interarrival time , 839 Expected monetary value criterion , 754n Expected payoff , decision trees , 767-769 Expected ...
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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