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 423
... path from the source to the sink in the residual network such that every arc on this path has strictly positive residual ... augmenting path . Increase by c * the residual capacity of each arc in the opposite direction on this augmenting path ...
... path from the source to the sink in the residual network such that every arc on this path has strictly positive residual ... augmenting path . Increase by c * the residual capacity of each arc in the opposite direction on this augmenting path ...
Page 424
... augmenting path . ) Therefore , for the following example ( and the prob- lems at the end of the chapter ) , the selection is just made arbitrarily . Applying This Algorithm to the Seervada Park Maximum Flow Problem Applying this ...
... augmenting path . ) Therefore , for the following example ( and the prob- lems at the end of the chapter ) , the selection is just made arbitrarily . Applying This Algorithm to the Seervada Park Maximum Flow Problem Applying this ...
Page 426
... augmenting paths remain ( because the real unused arc capacity for E → B is zero ) . Therefore , the refinement permits us to add the flow assignment of 1 for O → C → E ... augmenting path for iteration 426 9 NETWORK OPTIMIZATION MODELS.
... augmenting paths remain ( because the real unused arc capacity for E → B is zero ) . Therefore , the refinement permits us to add the flow assignment of 1 for O → C → E ... augmenting path for iteration 426 9 NETWORK OPTIMIZATION MODELS.
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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 cost CPF solution CPLEX decision variables described 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 graphical identify increase initial BF solution integer interior-point iteration leaving basic variable linear programming model linear programming problem LINGO LP relaxation lution 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 shown simplex method slack variables solve the model Solver spreadsheet step subproblem surplus variables Table tion values 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 |