## 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 550

Table 11.2 gives the estimated probability that the respective teams will fail when

0 , 1 , or 2

scientists are considered because each new scientist will need to devote full ...

Table 11.2 gives the estimated probability that the respective teams will fail when

0 , 1 , or 2

**additional**scientists are added to that team . Only integer numbers ofscientists are considered because each new scientist will need to devote full ...

Page 617

Using square brackets to denote [ x * ] = greatest integer = x , we have for the

range of values for the two new subproblems x ; < ( x * ] and X ; = [ x * ] + 1 ,

respectively . Each inequality becomes an

subproblem .

Using square brackets to denote [ x * ] = greatest integer = x , we have for the

range of values for the two new subproblems x ; < ( x * ] and X ; = [ x * ] + 1 ,

respectively . Each inequality becomes an

**additional**constraint for that newsubproblem .

Page 715

( d ) Now suppose that there is an

products 1 and 2 must total at least $ 9,000 . Use the technique presented in the “

Extensions ” subsection of Sec . 13.8 to add this constraint to the model

formulated in ...

( d ) Now suppose that there is an

**additional**constraint that the profit fromproducts 1 and 2 must total at least $ 9,000 . Use the technique presented in the “

Extensions ” subsection of Sec . 13.8 to add this constraint to the model

formulated in ...

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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