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 34
... feasible solutions , while the points ( 1 , 3 ) and ( 4 , 4 ) are infeasible solutions . The feasible region is the collection of all feasible solutions . The feasible region in the example is the entire shaded area in Fig . 3.2 . It is ...
... feasible solutions , while the points ( 1 , 3 ) and ( 4 , 4 ) are infeasible solutions . The feasible region is the collection of all feasible solutions . The feasible region in the example is the entire shaded area in Fig . 3.2 . It is ...
Page 35
... solutions will have an infi- nite number of them , each with the same optimal value of the objective function . Another possibility is that a problem has no optimal solutions . This occurs only if ( 1 ) it has no feasible solutions or ...
... solutions will have an infi- nite number of them , each with the same optimal value of the objective function . Another possibility is that a problem has no optimal solutions . This occurs only if ( 1 ) it has no feasible solutions or ...
Page 236
... feasible solutions for the two problems . For any such pair of feasible solutions , this inequality must hold because the maximum feasible value of Z = cx ( 36 ) equals the min- imum feasible value of the dual objective function W = yb ...
... feasible solutions for the two problems . For any such pair of feasible solutions , this inequality must hold because the maximum feasible value of Z = cx ( 36 ) equals the min- imum feasible value of the dual objective function W = yb ...
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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 corresponding cost Courseware CPF solution CPLEX decision variables dual problem dynamic programming entering basic variable estimates example feasible region feasible solutions final simplex tableau final tableau flow following problem formulation functional constraints Gaussian elimination given graphical identify increase initial BF solution integer interior-point iteration leaving basic variable linear programming model linear programming problem LINGO LP relaxation lution Maximize subject 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 right-hand sides sensitivity analysis shadow prices simplex method slack variables solve the model Solver spreadsheet step subproblem surplus variables Table tion values weeks Wyndor Glass x₁ zero