Introduction to Operations ResearchCD-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 734
... payoff for player 1 = m n i = 1 j = 1 PijXiYj where Pij is the payoff if player 1 uses pure strategy i and player 2 uses pure strategy j . In the example of mixed strategies just given , there are four possible payoffs ( −2 , 2 , 4 ...
... payoff for player 1 = m n i = 1 j = 1 PijXiYj where Pij is the payoff if player 1 uses pure strategy i and player 2 uses pure strategy j . In the example of mixed strategies just given , there are four possible payoffs ( −2 , 2 , 4 ...
Page 764
... 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 experimentation ...
... 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 experimentation ...
Page 767
... payoff by multiplying the expected payoff of each branch ( shown in boldface to the right of the branch ) by the probability of that branch and then summing these products . Record this expected payoff for each deci- sion fork in ...
... payoff by multiplying the expected payoff of each branch ( shown in boldface to the right of the branch ) by the probability of that branch and then summing these products . Record this expected payoff for each deci- sion fork in ...
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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 Courseware CPLEX decision variables dual problem dual simplex method dynamic programming entering basic variable estimates example feasible region feasible solutions final simplex tableau final tableau following problem formulation functional constraints Gaussian elimination given goal programming graphical identify increase initial BF solution integer 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 resource right-hand sides sensitivity analysis shadow prices shown slack variables solve the model Solver spreadsheet step subproblem surplus variables Table tion unit profit values weeks Wyndor Glass x₁ zero