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 333
... goal sets an upper limit that we do not want to exceed ( but falling under the limit is fine ) . 3. A two - sided goal sets a specific target that we do not ... goal programming 7.5 LINEAR GOAL PROGRAMMING AND ITS SOLUTION PROCEDURES 333.
... goal sets an upper limit that we do not want to exceed ( but falling under the limit is fine ) . 3. A two - sided goal sets a specific target that we do not ... goal programming 7.5 LINEAR GOAL PROGRAMMING AND ITS SOLUTION PROCEDURES 333.
Page 345
... goals in a goal programming prob- lem is expressed algebraically as 3x1 + 4x2 + 2x3 = 60 , where 60 is the specific numeric goal and the left - hand side gives the level achieved toward meeting this goal . ( a ) Letting y be the amount ...
... goals in a goal programming prob- lem is expressed algebraically as 3x1 + 4x2 + 2x3 = 60 , where 60 is the specific numeric goal and the left - hand side gives the level achieved toward meeting this goal . ( a ) Letting y be the amount ...
Page 346
... goal established by the government for each of these factors . Contribution per 1,000 Acres Crop : Factor 1 2 3 Foreign capital $ 3,000 $ 5,000 Citizens fed 150 Citizens employed 10 75 15 $ 4,000 100 12 Goal > $ 70,000,000 ≥ 1,750,000 ...
... goal established by the government for each of these factors . Contribution per 1,000 Acres Crop : Factor 1 2 3 Foreign capital $ 3,000 $ 5,000 Citizens fed 150 Citizens employed 10 75 15 $ 4,000 100 12 Goal > $ 70,000,000 ≥ 1,750,000 ...
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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