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 54
... decision variables , we can develop the objective function and the constraints on the values of these decision variables . In this particular problem , the decisions to be made are well defined , but the appro- priate means of conveying ...
... decision variables , we can develop the objective function and the constraints on the values of these decision variables . In this particular problem , the decisions to be made are well defined , but the appro- priate means of conveying ...
Page 75
... decision variables , as outlined below . Decision Variables . 10,000 production variables : one for each combination of a plant , machine , product , and month 1,000 inventory variables : one for each combination of a plant , product ...
... decision variables , as outlined below . Decision Variables . 10,000 production variables : one for each combination of a plant , machine , product , and month 1,000 inventory variables : one for each combination of a plant , product ...
Page 576
... decision variables . In many practical problems , the decision variables actu- ally make sense only if they have integer values . For example , it is often necessary to as- sign people , machines , and vehicles to activities in integer ...
... decision variables . In many practical problems , the decision variables actu- ally make sense only if they have integer values . For example , it is often necessary to as- sign people , machines , and vehicles to activities in integer ...
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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