## 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 222

( b ) Develop a table giving each of the CPF solutions and the

( b ) Develop a table giving each of the CPF solutions and the

**corresponding**defining equations , BF solution , and nonbasic variables . Calculate Z for each of these solutions , and use just this information to identify the optimal ...Page 235

Before this goal has been reached , the

Before this goal has been reached , the

**corresponding**y in row 0 ( coefficients of slack variables ) of the current tableau must be infeasible for the dual problem . However , after the goal is reached , the**corresponding**y must be an ...Page 252

With the Big M method , since M has been added initially to the coefficient of each artificial variable in row 0 , the current value of each

With the Big M method , since M has been added initially to the coefficient of each artificial variable in row 0 , the current value of each

**corresponding**dual variable is the current co- efficient of this artificial variable minus M.### What people are saying - Write a review

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### 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 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