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
of or all the arcs in the network are directed arcs , we then distinguish between directed paths and undirected paths . A directed path from node i to node ; is a sequence of connecting arcs whose direction ( if any ) is toward node j ...
of or all the arcs in the network are directed arcs , we then distinguish between directed paths and undirected paths . A directed path from node i to node ; is a sequence of connecting arcs whose direction ( if any ) is toward node j ...
Page 423
An augmenting path is a directed path from the source to the sink in the residual network such that every arc on this path has strictly positive residual capacity . The mini- mum of these residual capacities is called the residual ...
An augmenting path is a directed path from the source to the sink in the residual network such that every arc on this path has strictly positive residual capacity . The mini- mum of these residual capacities is called the residual ...
Page 476
TABLE 10.2 The paths and path lengths through Reliable's project network Path ABC → DG → H → M FINISH ABC - E - HM → FINISH START START START START START A B C → E → FJK → N → FINISH ABC - E →→→ L → N → FINISH ABC →→→ K ...
TABLE 10.2 The paths and path lengths through Reliable's project network Path ABC → DG → H → M FINISH ABC - E - HM → FINISH START START START START START A B C → E → FJK → N → FINISH ABC - E →→→ L → N → FINISH ABC →→→ K ...
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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