## 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 |

### From inside the book

Results 1-3 of 64

Page 486

In reality , the duration of each activity is a random variable having some

probability

account by using three different types of estimates of the duration of an activity to

obtain ...

In reality , the duration of each activity is a random variable having some

probability

**distribution**. The original version of PERT took this uncertainty intoaccount by using three different types of estimates of the duration of an activity to

obtain ...

Page 879

Specifically , this

and ( 1 – p ) , for which kind of repair will ... each kind has an exponential

different .

Specifically , this

**distribution**would assume that there are fixed probabilities , pand ( 1 – p ) , for which kind of repair will ... each kind has an exponential

**distribution**, but that the parameters for these two exponential**distributions**aredifferent .

Page 1146

( b ) Now do this by using the table for the normal

and applying the inverse transformation method . R 22.4-15 . Obtaining uniform

random numbers as instructed at the beginning of the Problems section ...

( b ) Now do this by using the table for the normal

**distribution**given in Appendix 5and applying the inverse transformation method . R 22.4-15 . Obtaining uniform

random numbers as instructed at the beginning of the Problems section ...

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### Common terms and phrases

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