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 195
An edge of the feasible region is a feasible line seg- ment that lies at the intersection of n 1 constraint boundaries , where each endpoint lies on one additional constraint boundary ( so that these endpoints are CPF solutions ) .
An edge of the feasible region is a feasible line seg- ment that lies at the intersection of n 1 constraint boundaries , where each endpoint lies on one additional constraint boundary ( so that these endpoints are CPF solutions ) .
Page 198
largement of the feasible region to the right of ( 3 , 5 ) . Consequently , the adjacent CPF so- lutions for ( 2 , 6 ) now are ( 0 , 6 ) and ( 3 , 5 ) , and again neither is better than ( 2 , 6 ) . How- ever , another CPF solution ( 4 ...
largement of the feasible region to the right of ( 3 , 5 ) . Consequently , the adjacent CPF so- lutions for ( 2 , 6 ) now are ( 0 , 6 ) and ( 3 , 5 ) , and again neither is better than ( 2 , 6 ) . How- ever , another CPF solution ( 4 ...
Page 703
There actually are two main versions of SUMT , one of which is an exterior - point algorithm that deals with infeasible solutions while using a penalty function to force convergence to the feasible region . We shall describe the other ...
There actually are two main versions of SUMT , one of which is an exterior - point algorithm that deals with infeasible solutions while using a penalty function to force convergence to the feasible region . We shall describe the other ...
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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