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 74
This corporation has 10 plants in various parts of the world . Each of these plants produces the same 10 products and then sells them within its region . The demand ( sales potential ) for each of these products from each plant is known ...
This corporation has 10 plants in various parts of the world . Each of these plants produces the same 10 products and then sells them within its region . The demand ( sales potential ) for each of these products from each plant is known ...
Page 103
One plant outside Detroit , MI , assembles two models from the family of midsized and luxury cars . The first model , the Family Thrillseeker , is a four - door sedan with vinyl seats , plastic interior , standard features ...
One plant outside Detroit , MI , assembles two models from the family of midsized and luxury cars . The first model , the Family Thrillseeker , is a four - door sedan with vinyl seats , plastic interior , standard features ...
Page 388
There are more products ( four ) than plants ( three ) , so one of the plants will need to be assigned two products . Plant 3 has only enough excess capacity to produce one prod- uct ( see Table 8.27 ) , so either Plant 1 or Plant 2 ...
There are more products ( four ) than plants ( three ) , so one of the plants will need to be assigned two products . Plant 3 has only enough excess capacity to produce one prod- uct ( see Table 8.27 ) , so either Plant 1 or Plant 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