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 318
Therefore , having a large number of upper bound constraints among the functional constraints greatly increases the computational effort required . The upper bound technique avoids this increased effort by removing the upper bound ...
Therefore , having a large number of upper bound constraints among the functional constraints greatly increases the computational effort required . The upper bound technique avoids this increased effort by removing the upper bound ...
Page 319
Replace x ; by u ; – yj , where 0 = y ; Suj . u < The upper bound technique uses the following rule to make this choice : Rule : Begin with choice 1 . Whenever x ; 0 , use choice 1 , so x ; is nonbasic . Whenever x ; = Uj , use choice 2 ...
Replace x ; by u ; – yj , where 0 = y ; Suj . u < The upper bound technique uses the following rule to make this choice : Rule : Begin with choice 1 . Whenever x ; 0 , use choice 1 , so x ; is nonbasic . Whenever x ; = Uj , use choice 2 ...
Page 607
9 ( 0 , 1 , 0 , 1 ) LP relaxation of subproblem 2 : ( x1 , X2 , X3 , X4 ) = ( 1,5.0.5 ) with 2 = 16 Z = All 16 ( à 1,0,1 ) 5 1 The resulting bounds for the subproblems then are Bound for subproblem 1 : Z < 9 , Bound for subproblem 2 ...
9 ( 0 , 1 , 0 , 1 ) LP relaxation of subproblem 2 : ( x1 , X2 , X3 , X4 ) = ( 1,5.0.5 ) with 2 = 16 Z = All 16 ( à 1,0,1 ) 5 1 The resulting bounds for the subproblems then are Bound for subproblem 1 : Z < 9 , Bound for subproblem 2 ...
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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 allocation allowable range artificial variables assignment problem augmenting path basic solution Big M method changes coefficients column Consider the following constraint boundary corresponding CPLEX decision variables 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 graphically identify increase initial BF solution integer interior-point iteration leaving basic variable linear programming model linear programming problem LP relaxation lution Maximize Maximize Z maximum flow problem Minimize needed node nonbasic variables objective function obtained optimal solution optimality test path Plant presented in Sec primal problem Prob procedure range to stay resource right-hand sides sensitivity analysis shadow prices slack variables solve this model Solver spreadsheet step subproblem surplus variables tion transportation problem transportation simplex method weeks Wyndor Glass x₁ zero
Bibliographic information
| Title | Introduction to Operations Research, Volume 1 Introduction to Operations Research, Frederick S. Hillier 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 | the University of Michigan |
| Digitized | 8 Aug 2011 |
| ISBN | 0072321695, 9780072321692 |
| Length | 1214 pages |
| Export Citation | BiBTeX EndNote RefMan |