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 333
An upper , one - sided goal sets an upper limit that we do not want to exceed ( but falling under the limit is fine ) . 3. A two - sided goal sets a specific target that we do not want to miss on either side . Goal programming problems ...
An upper , one - sided goal sets an upper limit that we do not want to exceed ( but falling under the limit is fine ) . 3. A two - sided goal sets a specific target that we do not want to miss on either side . Goal programming problems ...
Page 333
An upper , one - sided goal sets an upper limit that we do not want to exceed ( but falling under the limit is fine ) . 3. A two - sided goal sets a specific target that we do not want to miss on either side . Goal programming problems ...
An upper , one - sided goal sets an upper limit that we do not want to exceed ( but falling under the limit is fine ) . 3. A two - sided goal sets a specific target that we do not want to miss on either side . Goal programming problems ...
Page 346
Why ? c ( b ) Management is wondering what would happen if the total profit goal were to be increased to wanting at least $ 140 million ( without any change in the original penalty weights ) . Solve the revised model with this change ...
Why ? c ( b ) Management is wondering what would happen if the total profit goal were to be increased to wanting at least $ 140 million ( without any change in the original penalty weights ) . Solve the revised model with this change ...
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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