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. |
From inside the book
Results 1-3 of 85
Page 391
Frederick S. Hillier, Gerald J. Lieberman. 8.4 CONCLUSIONS The linear programming ... problem types have such simple formulations that they can be solved much ... programming problems studied in this chapter , namely , the ...
Frederick S. Hillier, Gerald J. Lieberman. 8.4 CONCLUSIONS The linear programming ... problem types have such simple formulations that they can be solved much ... programming problems studied in this chapter , namely , the ...
Page 601
... problem . This guarantee is the key to the remarkable efficiency of the simplex method . As a result , linear programming prob- lems generally are much easier to solve than IP problems . Consequently , most successful algorithms for ...
... problem . This guarantee is the key to the remarkable efficiency of the simplex method . As a result , linear programming prob- lems generally are much easier to solve than IP problems . Consequently , most successful algorithms for ...
Page 717
... programming formulation of this problem ( with x1 and x2 as decision variables ) has the same form as the main case ... linear programming model where it is feasible to use OT even when the RT capacity at that plant is not fully ...
... programming formulation of this problem ( with x1 and x2 as decision variables ) has the same form as the main case ... linear programming model where it is feasible to use OT even when the RT capacity at that plant is not fully ...
Other editions - View all
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 corresponding cost Courseware CPF solution CPLEX decision variables dual problem dynamic programming entering basic variable estimates example feasible region feasible solutions final simplex tableau final tableau flow following problem formulation functional constraints Gaussian elimination given graphical identify increase initial BF solution integer interior-point iteration leaving basic variable linear programming model linear programming problem LINGO LP relaxation lution Maximize subject 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 simplex method slack variables solve the model Solver spreadsheet step subproblem surplus variables Table tion values weeks Wyndor Glass x₁ zero