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 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 ...
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Introduction to Operations Research Frederick S. Hillier,Gerald J. Lieberman No preview available - 2001 |
Common terms and phrases
activity algebraic algorithm allocation allowable range artificial variables assignment problem augmenting path basic solution Big M method changes coefficients column Consider the following constraint boundary corresponding CPLEX decision variables dual problem dynamic programming entering basic variable example feasible region feasible solutions final simplex tableau final tableau following problem formulation functional constraints Gaussian elimination given goal goal programming graphically identify increase initial BF solution integer interior-point iteration leaving basic variable linear programming model linear programming problem LP relaxation lution Maximize Maximize Z maximum flow problem Minimize needed node nonbasic variables objective function obtained optimal solution optimality test path Plant presented in Sec primal problem Prob procedure range to stay resource right-hand sides sensitivity analysis shadow prices slack variables solve this model Solver spreadsheet step subproblem surplus variables tion transportation problem transportation simplex method weeks Wyndor Glass x₁ zero
Bibliographic information
| Title | Introduction to Operations Research, Volume 1 Introduction to Operations Research, Frederick S. Hillier McGraw-Hill industrial & plant engineering series McGraw-Hill series in industrial engineering and management science |
| Authors | Frederick S. Hillier, Gerald J. Lieberman |
| Edition | 7, illustrated |
| Publisher | McGraw-Hill, 2001 |
| Original from | the University of Michigan |
| Digitized | 8 Aug 2011 |
| ISBN | 0072321695, 9780072321692 |
| Length | 1214 pages |
| Export Citation | BiBTeX EndNote RefMan |