## 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 65

Page 486

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

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

**distribution**. The original version of PERT took this uncertainty into account by using three different types of estimates of the duration of an ...Page 879

case except k = 1 ( exponential

case except k = 1 ( exponential

**distribution**) , which has o = 1 / u . To illustrate a typical situation where o > 1/4 can occur , we suppose that the service involved in the queueing system is the repair of some kind of machine or ...Page 1146

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

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

**distribution**given in Appendix 5 and applying the inverse transformation method . R 22.4-15 . Obtaining uniform random numbers as instructed at the beginning of the Problems section ...### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Other editions - View all

### Common terms and phrases

activity additional algorithm allowable amount apply assignment basic solution basic variable BF solution bound boundary called changes coefficients column complete Consider constraints Construct corresponding cost CPF solution decision variables demand described determine distribution dual problem entering equal equations estimates example feasible feasible region 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 shown shows side simplex method simplex tableau slack solve step supply Table tableau tion unit weeks Wyndor Glass zero