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 225
and and x , ≥0 , j = 1 , . . . , 5 . lution are X3 , X1 , You are given the facts that the basic variables in the optimal so- and x5 and that -1 3 1 11 - -3 1 X2 ≥ 0 , X3 ≥ 0 . Let x4 , xs , and x6 denote the slack variables for the ...
and and x , ≥0 , j = 1 , . . . , 5 . lution are X3 , X1 , You are given the facts that the basic variables in the optimal so- and x5 and that -1 3 1 11 - -3 1 X2 ≥ 0 , X3 ≥ 0 . Let x4 , xs , and x6 denote the slack variables for the ...
Page 786
You are given the following payoff table ( in units of dollars ) : Alternative A1 A2 Prior probability S1 400 0 0.4 State of Nature S2 -100 100 0.6 You have the option of paying $ 100 to have research done to bet- ter predict which ...
You are given the following payoff table ( in units of dollars ) : Alternative A1 A2 Prior probability S1 400 0 0.4 State of Nature S2 -100 100 0.6 You have the option of paying $ 100 to have research done to bet- ter predict which ...
Page 1187
... given win 0.45 win and win lose , given win 0.25 0.15 win and lose 0.818 win , given win 0.333 win , given lose Lose 0.4 0.25 win , given lose 0.1 lose and win 0.182 lose , given win lose , given lose 0.75 0.3 ...
... given win 0.45 win and win lose , given win 0.25 0.15 win and lose 0.818 win , given win 0.333 win , given lose Lose 0.4 0.25 win , given lose 0.1 lose and win 0.182 lose , given win lose , given lose 0.75 0.3 ...
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Introduction to Operations Research Frederick S. Hillier,Gerald J. Lieberman No preview available - 2001 |
Common terms and phrases
activity additional algorithm allowable amount apply assigned basic solution basic variable BF solution bound boundary called changes coefficients column complete Consider Construct corresponding cost CPF solution decision variables described determine developed dual problem entering equations estimates example feasible feasible region feasible solutions FIGURE final flow formulation functional constraints given gives goal identify illustrate increase indicates initial iteration linear programming linear programming model Maximize million Minimize month needed node objective function obtained operations optimal optimal solution original parameters path perform plant possible presented primal problem Prob procedure profit programming problem provides range resource respective resulting revised sensitivity analysis shown shows side simplex method simplex tableau slack solve step Table tableau tion unit values weeks Wyndor Glass x₁ zero