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 120
As indicated next , we call x2 the entering basic variable for iteration 1 . At any iteration of the simplex method , the purpose of step 1 is to choose one nonbasic variable to increase from zero ( while the values of the basic ...
As indicated next , we call x2 the entering basic variable for iteration 1 . At any iteration of the simplex method , the purpose of step 1 is to choose one nonbasic variable to increase from zero ( while the values of the basic ...
Page 178
Also identify the initial entering basic variable and the leaving basic variable . I ( c ) Continue from part ( b ) to work through the simplex method step by step to solve the problem . 4.6-2 . Consider the following problem .
Also identify the initial entering basic variable and the leaving basic variable . I ( c ) Continue from part ( b ) to work through the simplex method step by step to solve the problem . 4.6-2 . Consider the following problem .
Page 377
Increasing the entering basic variable from zero sets off a chain reaction of compensating changes in other basic variables ( allocations ) , in order to continue satisfy- ing the supply and demand constraints . The first basic variable ...
Increasing the entering basic variable from zero sets off a chain reaction of compensating changes in other basic variables ( allocations ) , in order to continue satisfy- ing the supply and demand constraints . The first basic variable ...
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Introduction to Operations Research Frederick S. Hillier,Gerald J. Lieberman No preview available - 2001 |
Common terms and phrases
activity additional algorithm allowable amount apply assigned basic solution basic variable BF solution bound boundary called changes coefficients column complete Consider Construct corresponding cost CPF solution decision variables described determine developed dual problem entering equations estimates example feasible feasible region feasible solutions FIGURE final flow formulation functional constraints given gives goal identify illustrate increase indicates initial iteration linear programming linear programming model Maximize million Minimize month needed node objective function obtained operations optimal optimal solution original parameters path perform plant possible presented primal problem Prob procedure profit programming problem provides range resource respective resulting revised sensitivity analysis shown shows side simplex method simplex tableau slack solve step Table tableau tion unit values weeks Wyndor Glass x₁ zero