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. |
From inside the book
Results 1-3 of 88
Page 156
3.3 , we pointed out that the values used for the model parameters ( the aij , bi , and c ; identified in Table 3.3 ) generally are just estimates of quantities whose true values will not become known until the linear programming study ...
3.3 , we pointed out that the values used for the model parameters ( the aij , bi , and c ; identified in Table 3.3 ) generally are just estimates of quantities whose true values will not become known until the linear programming study ...
Page 255
a existent , so that the parameters in the original formulation may represent little more than quick rules of thumb provided by harassed line personnel . The data may even represent deliberate overestimates or underestimates to protect ...
a existent , so that the parameters in the original formulation may represent little more than quick rules of thumb provided by harassed line personnel . The data may even represent deliberate overestimates or underestimates to protect ...
Page 284
Because 0 = 1 is the maximum realistic value of 0 , this indicates that c , and c2 together are insensitive parameters with respect to the Variation 2 model in Table 6.21 . There is no need to try to estimate these parameters more ...
Because 0 = 1 is the maximum realistic value of 0 , this indicates that c , and c2 together are insensitive parameters with respect to the Variation 2 model in Table 6.21 . There is no need to try to estimate these parameters more ...
What people are saying - Write a review
We haven't found any reviews in the usual places.
Other editions - View all
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