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 177
... feasible region is unbounded . ( b ) If the objective is to maximize Z -x1 + x2 , does the model have an optimal solution ? If so , find it . If not , explain why not . ( c ) Repeat part ( b ) when the objective is to maximize Z = X ...
... feasible region is unbounded . ( b ) If the objective is to maximize Z -x1 + x2 , does the model have an optimal solution ? If so , find it . If not , explain why not . ( c ) Repeat part ( b ) when the objective is to maximize Z = X ...
Page 195
... feasible region is a feasible line seg- ment that lies at the intersection of n 1 constraint boundaries , where each endpoint lies on one additional constraint boundary ( so that these endpoints are CPF solutions ) . Two CPF ...
... feasible region is a feasible line seg- ment that lies at the intersection of n 1 constraint boundaries , where each endpoint lies on one additional constraint boundary ( so that these endpoints are CPF solutions ) . Two CPF ...
Page 198
... feasible region goes down from ( 2 , 6 ) to ( 3 , 5 ) and then " bends outward " to ( 4 , 5 ) , beyond the objective function line passing through ( 2 , 6 ) . 8 The key point is that the kind of situation illustrated in Fig . 5.3 can ...
... feasible region goes down from ( 2 , 6 ) to ( 3 , 5 ) and then " bends outward " to ( 4 , 5 ) , beyond the objective function line passing through ( 2 , 6 ) . 8 The key point is that the kind of situation illustrated in Fig . 5.3 can ...
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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 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