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 xxvii
13 , but the only background needed for this is presented in Appendix 4. For Chaps . 15 to 22 ( probabilistic models ) , a previous introduction to probability theory is assumed , and calculus is used in a few places .
13 , but the only background needed for this is presented in Appendix 4. For Chaps . 15 to 22 ( probabilistic models ) , a previous introduction to probability theory is assumed , and calculus is used in a few places .
Page 227
( a ) Use the fundamental insight presented in Sec . 5.3 to identify the missing numbers in the current simplex tableau . Show your calculations . ( b ) Indicate which of these missing numbers would be generated by the revised simplex ...
( a ) Use the fundamental insight presented in Sec . 5.3 to identify the missing numbers in the current simplex tableau . Show your calculations . ( b ) Indicate which of these missing numbers would be generated by the revised simplex ...
Page 405
For example , both the transportation problem and the assignment problem discussed in the preceding chapter fall into this category because of their network representations presented in Figs . 8.3 and 8.5 . One of the linear programming ...
For example , both the transportation problem and the assignment problem discussed in the preceding chapter fall into this category because of their network representations presented in Figs . 8.3 and 8.5 . One of the linear programming ...
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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