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 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 336
Thus , the initial focus should be on achieving as closely as possible these first - priority goals . ... we deal with goals on the same priority level , our approach is just like the one described for nonpreemptive goal programming .
Thus , the initial focus should be on achieving as closely as possible these first - priority goals . ... we deal with goals on the same priority level , our approach is just like the one described for nonpreemptive goal programming .
Page 346
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 .
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 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