## 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 301

Given 0 , the coefficients of xı in the model become Ci 9 + 907 = ( a ) Construct the

Given 0 , the coefficients of xı in the model become Ci 9 + 907 = ( a ) Construct the

**resulting**revised final tableau ( as a function of 0 ) , and then convert this tableau to proper form from Gaussian elimination .Page 791

( c ) Apply the backward induction procedure , and identify the

( c ) Apply the backward induction procedure , and identify the

**resulting**optimal policy . a A 15.4-8 . Jose Morales manages a large outdoor fruit stand in one of the less affluent neighborhoods of San Jose , California .Page 1082

Use the

Use the

**resulting**optimal solution to identify an optimal policy . , 1 21.4-6 . Use the policy improvement algorithm to find an optimal policy for Prob . 21.2-6 . D.1 21.4-7 . Use the policy improvement algorithm to find an optimal ...### 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