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
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Page 730
... Strategy 1 2 3 1 1 2 4 Player 1 2 1 0 5 3 1 -1 A strategy is dominated by a second strategy if the second strategy is always at least as good ( and sometimes better ) regardless of what the opponent does . A dominated strategy can be ...
... Strategy 1 2 3 1 1 2 4 Player 1 2 1 0 5 3 1 -1 A strategy is dominated by a second strategy if the second strategy is always at least as good ( and sometimes better ) regardless of what the opponent does . A dominated strategy can be ...
Page 731
... strategy 1. Player 1 then will receive a payoff of 1 from player 2 ( that is , politician 1 will gain 1,000 votes from politician 2 ) . In general , the payoff to player 1 when both players play optimally is referred to as the value of ...
... strategy 1. Player 1 then will receive a payoff of 1 from player 2 ( that is , politician 1 will gain 1,000 votes from politician 2 ) . In general , the payoff to player 1 when both players play optimally is referred to as the value of ...
Page 734
... strategy 1 or 2 , but he is discarding strategy 3 en- tirely . Similarly , player 2 is randomly choosing between his last two pure strategies . To play the game , each player could then flip a coin to determine which of his two accept ...
... strategy 1 or 2 , but he is discarding strategy 3 en- tirely . Similarly , player 2 is randomly choosing between his last two pure strategies . To play the game , each player could then flip a coin to determine which of his two accept ...
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