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 102
... model in algebraic form that shows the individual constraints and decision vari- ables for this problem . c ( b ) Formulate this same model on an Excel spreadsheet instead . Then use the Excel Solver to solve the model . c ( c ) Use MPL ...
... model in algebraic form that shows the individual constraints and decision vari- ables for this problem . c ( b ) Formulate this same model on an Excel spreadsheet instead . Then use the Excel Solver to solve the model . c ( c ) Use MPL ...
Page 162
... solve the model , as described in Sec . 3.6 . The more powerful spread- sheet solvers can solve fairly large models with many thousand decision variables . How- ever , when the spreadsheet grows to an unwieldy size 162 4 SOLVING LINEAR ...
... solve the model , as described in Sec . 3.6 . The more powerful spread- sheet solvers can solve fairly large models with many thousand decision variables . How- ever , when the spreadsheet grows to an unwieldy size 162 4 SOLVING LINEAR ...
Page 1082
... solve this model . Use the re- sulting optimal solution to identify an optimal policy . 21.3-4 . Reconsider Prob . 21.2-4 . ( a ) Formulate a linear programming model for finding an optimal policy . c ( b ) Use the simplex method to solve ...
... solve this model . Use the re- sulting optimal solution to identify an optimal policy . 21.3-4 . Reconsider Prob . 21.2-4 . ( a ) Formulate a linear programming model for finding an optimal policy . c ( b ) Use the simplex method to solve ...
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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 CPF solution CPLEX decision variables described 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 graphical identify increase initial BF solution integer interior-point 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 right-hand sides sensitivity analysis shadow prices shown simplex method slack variables solve the model Solver spreadsheet step subproblem surplus variables Table tion values weeks Wyndor Glass x₁ zero