## Introduction to Operations Research, Volume 1-- This classic, field-defining text is the market leader in Operations Research -- and it's now updated and expanded to keep professionals a step ahead -- Features 25 new detailed, hands-on case studies added to the end of problem sections -- plus an expanded look at project planning and control with PERT/CPM -- A new, software-packed CD-ROM contains Excel files for examples in related chapters, numerous Excel templates, plus LINDO and LINGO files, along with MPL/CPLEX Software and MPL/CPLEX files, each showing worked-out examples |

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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 , x1 = 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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activity additional algorithm alternative amount analysis apply assignment assumed basic variable begin BF solution calculate called changes coefficients column complete Consider constraints Construct corresponding cost CPF solution customers decision demand described determine developed distribution entering equations estimated example expected feasible FIGURE final flow formulation given gives hour identify illustrate increase indicates initial inventory iteration linear programming machine Maximize mean million Minimize month needed node objective function obtained operations optimal optimal solution original parameter path payoff plant player possible presented Prob probability problem procedure profit programming problem queueing respectively resulting shown shows side simplex method solution solve step strategy Table tableau tion transportation unit waiting weeks