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 306
( f ) For the optimal solution obtained under each of the six scenarios ( including the good weather scenario considered in parts ( a ) to ( d ) ] , calculate what the family's monetary worth would be at the end of the year if each of ...
( f ) For the optimal solution obtained under each of the six scenarios ( including the good weather scenario considered in parts ( a ) to ( d ) ] , calculate what the family's monetary worth would be at the end of the year if each of ...
Page 397
Starting with the northwest corner rule , interactively apply the transportation simplex method to obtain an optimal solution ... For each of the three initial BF solutions obtained in part ( a ) , calculate the percentage by which its ...
Starting with the northwest corner rule , interactively apply the transportation simplex method to obtain an optimal solution ... For each of the three initial BF solutions obtained in part ( a ) , calculate the percentage by which its ...
Page 718
Consider the following linearly constrained convex gorithm to obtain exactly the same solution you found in part ( c ) ... Explain f ( x ) = 3x + 4x2 - x - x3 , why exactly the same results would be obtained on these two it- subject to ...
Consider the following linearly constrained convex gorithm to obtain exactly the same solution you found in part ( c ) ... Explain f ( x ) = 3x + 4x2 - x - x3 , why exactly the same results would be obtained on these two it- subject to ...
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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