## Introduction to Operations ResearchCD-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 155

Because the constraint on

solution ( 2 , 6 ) , there is a surplus of this ... beyond 4 cannot yield a new optimal

solution with a larger value of Z. By contrast , the constraints on

3 ...

Because the constraint on

**resource**1 , x , s 4 , is not binding on the optimalsolution ( 2 , 6 ) , there is a surplus of this ... beyond 4 cannot yield a new optimal

solution with a larger value of Z. By contrast , the constraints on

**resources**2 and3 ...

Page 181

( b ) Use graphical analysis to find the shadow prices for the

Determine how many additional units of

the optimal value of Z by 15 . 4.7-5 . Consider the following problem . Maximize Z

= x1 ...

( b ) Use graphical analysis to find the shadow prices for the

**resources**. ( c )Determine how many additional units of

**resource**1 would be needed to increasethe optimal value of Z by 15 . 4.7-5 . Consider the following problem . Maximize Z

= x1 ...

Page 241

erates at a strictly positive level ( x ; > 0 ) , the marginal value of the

consumes must equal ( as opposed to exceeding ) the unit profit from this activity

. The second statement implies that the marginal value of

erates at a strictly positive level ( x ; > 0 ) , the marginal value of the

**resources**itconsumes must equal ( as opposed to exceeding ) the unit profit from this activity

. The second statement implies that the marginal value of

**resource**i is zero ( y ...### What people are saying - Write a review

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### Common terms and phrases

activity additional algorithm allocation allowable amount apply assignment basic solution basic variable BF solution bound boundary called changes coefficients column complete Consider constraint Construct corresponding cost CPF solution decision variables demand described determine distribution dual problem entering equal equations estimates example feasible feasible region feasible solutions FIGURE final flow formulation functional constraints given gives goal identify illustrate increase indicates initial iteration linear programming Maximize million Minimize month needed node nonbasic variables objective function obtained operations optimal optimal solution original parameters path plant possible presented primal problem Prob procedure profit programming problem provides range remaining resource respective resulting revised shown shows side simplex method simplex tableau slack solve step supply Table tableau tion unit values weeks Wyndor Glass zero