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 409
A directed path from node i to node j is a sequence of connecting arcs whose direction ( if any ) is toward nodej , so that flow from node i to node ; along this path is feasible . An undirected path from node i to node j is a sequence ...
A directed path from node i to node j is a sequence of connecting arcs whose direction ( if any ) is toward nodej , so that flow from node i to node ; along this path is feasible . An undirected path from node i to node j is a sequence ...
Page 412
Candidates for nth nearest node : Each solved node that is directly connected by a link to one or more unsolved nodes provides one candidate — the unsolved node with the shortest connecting link . ( Ties provide additional candidates . ) ...
Candidates for nth nearest node : Each solved node that is directly connected by a link to one or more unsolved nodes provides one candidate — the unsolved node with the shortest connecting link . ( Ties provide additional candidates . ) ...
Page 431
the network includes a dummy demand node that receives ( at zero cost ) all the unused supply capacity at the vendors . ... This application involves a company with several plants ( the supply nodes ) that can produce the same products ...
the network includes a dummy demand node that receives ( at zero cost ) all the unused supply capacity at the vendors . ... This application involves a company with several plants ( the supply nodes ) that can produce the same products ...
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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