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 116
For example , augmenting the solution ( 3 , 2 ) in the example yields the augmented solu- tion ( 3 , 2 , 1 , 8 , 5 ) because the corresponding values of the slack variables are x3 = 1 , X4 = 8 , and x5 5 . = A basic solution is an ...
For example , augmenting the solution ( 3 , 2 ) in the example yields the augmented solu- tion ( 3 , 2 , 1 , 8 , 5 ) because the corresponding values of the slack variables are x3 = 1 , X4 = 8 , and x5 5 . = A basic solution is an ...
Page 243
A key insight here is that the dual solution read from row 0 must also be a basic so- lution ! The reason is that the m basic variables for the primal problem are required to have a coefficient of zero in row 0 , which thereby requires ...
A key insight here is that the dual solution read from row 0 must also be a basic so- lution ! The reason is that the m basic variables for the primal problem are required to have a coefficient of zero in row 0 , which thereby requires ...
Page 245
solved directly to obtain this complementary solution . For example , consider the next - to- last primal basic solution in Table 6.9 , ( 4 , 6 , 0 , 0 , −6 ) . Note that x1 , x2 , and x5 are basic variables , since these variables are ...
solved directly to obtain this complementary solution . For example , consider the next - to- last primal basic solution in Table 6.9 , ( 4 , 6 , 0 , 0 , −6 ) . Note that x1 , x2 , and x5 are basic variables , since these variables are ...
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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
Bibliographic information
| Title | Introduction to Operations Research McGraw-Hill International Editions McGraw-Hill industrial & plant engineering series McGraw-Hill series in industrial engineering and management science |
| Authors | Frederick S. Hillier, Gerald J. Lieberman |
| Edition | 7, illustrated |
| Publisher | McGraw-Hill, 2001 |
| Original from | Pennsylvania State University |
| Digitized | 26 Aug 2009 |
| ISBN | 0071181636, 9780071181631 |
| Length | 1214 pages |
| Export Citation | BiBTeX EndNote RefMan |