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 556
... possible states to consider . We now have an infinite number of possible states ( 240 ≤ s3 ≤ 255 ) , so it is no longer feasible to solve separately for x for each possible value of $ 3 . Therefore , we instead have solved for x3 as a ...
... possible states to consider . We now have an infinite number of possible states ( 240 ≤ s3 ≤ 255 ) , so it is no longer feasible to solve separately for x for each possible value of $ 3 . Therefore , we instead have solved for x3 as a ...
Page 587
... possible constraints such that only some K of these constraints must hold . ( Assume that K < N. ) Part of the opti- mization process is to choose the combination of K constraints that permits the objective function to reach its best ...
... possible constraints such that only some K of these constraints must hold . ( Assume that K < N. ) Part of the opti- mization process is to choose the combination of K constraints that permits the objective function to reach its best ...
Page 1105
... possible values can be expressed as 000 , 001 , ... , 999. In such a case , the usual procedure still is to use m = 2b or m = 10a , so that an extremely large number of random integer numbers can be generated before the sequence starts ...
... possible values can be expressed as 000 , 001 , ... , 999. In such a case , the usual procedure still is to use m = 2b or m = 10a , so that an extremely large number of random integer numbers can be generated before the sequence starts ...
Other editions - View all
Introduction to Operations Research Frederick S. Hillier,Gerald J. Lieberman No preview available - 2001 |
Common terms and phrases
activity algebraic algorithm allowable range artificial variables b₂ basic solution c₁ c₂ changes coefficients column Consider the following cost Courseware CPLEX decision variables dual problem dual simplex method dynamic programming entering basic variable estimates example feasible region feasible solutions final simplex tableau final tableau following problem formulation functional constraints Gaussian elimination given goal programming graphical identify increase initial BF solution integer iteration leaving basic variable linear programming model linear programming problem LINGO LP relaxation lution Maximize Z maximum flow problem Minimize needed node nonbasic variables nonnegativity constraints objective function obtained optimal solution optimality test parameters path plant presented in Sec primal problem Prob procedure range to stay resource right-hand sides sensitivity analysis shadow prices shown slack variables solve the model Solver spreadsheet step subproblem surplus variables Table tion unit profit values weeks Wyndor Glass x₁ zero