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 266
4.7 , this range of values for b2 is referred to as its allowable range to stay
feasible . For any bị , recall from Sec . 4.7 that its allowable range to stay feasible
is the range of values over which the current optimal BF solution ' ( with adjusted
...
4.7 , this range of values for b2 is referred to as its allowable range to stay
feasible . For any bị , recall from Sec . 4.7 that its allowable range to stay feasible
is the range of values over which the current optimal BF solution ' ( with adjusted
...
Page 299
Determine the upper bound on 0 before the original optimal solution would
become nonoptimal . Then determine the best choice of 0 over this range . 6.7-26
. Consider the following parametric linear programming problem . Maximize Z ( O
) ...
Determine the upper bound on 0 before the original optimal solution would
become nonoptimal . Then determine the best choice of 0 over this range . 6.7-26
. Consider the following parametric linear programming problem . Maximize Z ( O
) ...
Page 634
Show profit ( after capital recovery costs are subtracted ) would be $ 4.2 how to
reformulate this restriction to fit an MIP model . million per long - range plane , $ 3
million per medium - range plane , and $ 2.3 million per short - range plane .
Show profit ( after capital recovery costs are subtracted ) would be $ 4.2 how to
reformulate this restriction to fit an MIP model . million per long - range plane , $ 3
million per medium - range plane , and $ 2.3 million per short - range plane .
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activity additional algorithm allocation allowable amount apply assignment basic solution basic variable BF solution bound boundary called changes coefficients column complete Consider constraint Construct corresponding cost CPF solution decision variables demand described determine distribution dual problem entering equal 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 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 revised shown shows side simplex method simplex tableau slack solve step supply Table tableau tion unit values weeks Wyndor Glass zero