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 177
4.5-5 , show that the following statements must be true for any linear programming problem that has a bounded feasible region and multiple optimal solutions : ( a ) Every convex combination of the optimal BF solutions must be optimal .
4.5-5 , show that the following statements must be true for any linear programming problem that has a bounded feasible region and multiple optimal solutions : ( a ) Every convex combination of the optimal BF solutions must be optimal .
Page 195
Consider any linear programming problem with n decision variables and a bounded feasible region . A CPF solution lies at the intersection of n constraint boundaries ( and satisfies the other constraints as well ) .
Consider any linear programming problem with n decision variables and a bounded feasible region . A CPF solution lies at the intersection of n constraint boundaries ( and satisfies the other constraints as well ) .
Page 198
largement of the feasible region to the right of ( 1 , 5 ) . Consequently , the adjacent CPF solutions for ( 2 , 6 ) now are ( 0 , 6 ) and ( , 5 ) , and again neither is better than ( 2 , 6 ) . However , another CPF solution ( 4 ...
largement of the feasible region to the right of ( 1 , 5 ) . Consequently , the adjacent CPF solutions for ( 2 , 6 ) now are ( 0 , 6 ) and ( , 5 ) , and again neither is better than ( 2 , 6 ) . However , another CPF solution ( 4 ...
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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