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 115
Frederick S. Hillier, Gerald J. Lieberman. variables . To illustrate , consider the first functional constraint in the Wyndor Glass Co. example of Sec . 3.1 X1 ≤ 4 . The slack variable for this constraint is defined to be X3 = 4- X1 ...
Frederick S. Hillier, Gerald J. Lieberman. variables . To illustrate , consider the first functional constraint in the Wyndor Glass Co. example of Sec . 3.1 X1 ≤ 4 . The slack variable for this constraint is defined to be X3 = 4- X1 ...
Page 214
... slack variables in rows 1 to 3 of the new tableau , because the coefficients of the slack variables in rows 1 to 3 of the initial tableau form an identity matrix . Thus , just as stated in the verbal description of the fundamental ...
... slack variables in rows 1 to 3 of the new tableau , because the coefficients of the slack variables in rows 1 to 3 of the initial tableau form an identity matrix . Thus , just as stated in the verbal description of the fundamental ...
Page 216
... slack variables in the final row 0— [ 0 , 2 , 1 ] . This calculation is shown below , where the first vector is row 0 of the initial tableau and the matrix is rows 1 to 3 of the initial tableau . 1 0 1 00 4 Final row 0 = [ -3 , -50 , 0 ...
... slack variables in the final row 0— [ 0 , 2 , 1 ] . This calculation is shown below , where the first vector is row 0 of the initial tableau and the matrix is rows 1 to 3 of the initial tableau . 1 0 1 00 4 Final row 0 = [ -3 , -50 , 0 ...
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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