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 382
The objective is to determine how all n assignments should be made to minimize the total cost . Any problem satisfying all these assumptions can be solved extremely efficiently by algorithms designed specifically for assignment problems ...
The objective is to determine how all n assignments should be made to minimize the total cost . Any problem satisfying all these assumptions can be solved extremely efficiently by algorithms designed specifically for assignment problems ...
Page 399
Consider the assignment problem having the following cost table . Task 1 2 3 Assignee А B C D 8 6 7 6 6 5 8 7 5 3 4 5 7 4 6 6 ( a ) Draw the network representation of this assignment problem . ( b ) Formulate this problem as a ...
Consider the assignment problem having the following cost table . Task 1 2 3 Assignee А B C D 8 6 7 6 6 5 8 7 5 3 4 5 7 4 6 6 ( a ) Draw the network representation of this assignment problem . ( b ) Formulate this problem as a ...
Page 400
( c ) Reformulate this assignment problem as an equivalent trans- portation problem with two sources and three destinations by constructing the appropriate parameter table . c ( d ) Obtain an optimal solution for the problem as ...
( c ) Reformulate this assignment problem as an equivalent trans- portation problem with two sources and three destinations by constructing the appropriate parameter table . c ( d ) Obtain an optimal solution for the problem as ...
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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