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 34
A feasible solution is a solution for which all the constraints are satisfied . An infeasible solution is a solution for which at least one constraint is violated . In the example , the points ( 2 , 3 ) and ( 4 , 1 ) in Fig .
A feasible solution is a solution for which all the constraints are satisfied . An infeasible solution is a solution for which at least one constraint is violated . In the example , the points ( 2 , 3 ) and ( 4 , 1 ) in Fig .
Page 177
Suppose that the following constraints have been provided for a linear programming model with decision variables xı a and X2 : a ( a ) Show that any convex combination of any set of feasible solutions must be a feasible solution ( so ...
Suppose that the following constraints have been provided for a linear programming model with decision variables xı a and X2 : a ( a ) Show that any convex combination of any set of feasible solutions must be a feasible solution ( so ...
Page 246
Yes No Yes Optimal Feasible ? Suboptimal Neither feasible nor superoptimal No Superoptimal To review the reasoning behind this property , note that the dual solution ( y * , z * – c ) must be feasible for the dual problem because the ...
Yes No Yes Optimal Feasible ? Suboptimal Neither feasible nor superoptimal No Superoptimal To review the reasoning behind this property , note that the dual solution ( y * , z * – c ) must be feasible for the dual problem because the ...
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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 assignment basic solution basic variable BF solution bound boundary called changes coefficients column complete Consider constraints Construct corresponding cost CPF solution decision variables demand described determine direction distribution dual problem entering equal equations estimates example feasible feasible region FIGURE final flow formulation functional constraints given gives goal identify illustrate increase indicates initial iteration linear programming Maximize million Minimize month needed node nonbasic variables objective function obtained operations optimal optimal solution original parameters path Plant possible presented primal problem Prob procedure profit programming problem provides range remaining resource respective resulting shown shows side simplex method simplex tableau slack solve step supply Table tableau tion unit weeks Wyndor Glass zero