## 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

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Page 19

Then , even as personnel

intervals to provide a specific numerical solution . ' This system usually is

computer - based . In fact , a considerable number of computer programs often

need to be ...

Then , even as personnel

**changes**, the system can be called on at regularintervals to provide a specific numerical solution . ' This system usually is

computer - based . In fact , a considerable number of computer programs often

need to be ...

Page 283

By checking the effect as o increases from 0 to 1 , we can determine just when

and how the optimal solution

these parameters increases . Considering these

especially ...

By checking the effect as o increases from 0 to 1 , we can determine just when

and how the optimal solution

**changes**as the error in the original estimates ofthese parameters increases . Considering these

**changes**simultaneously isespecially ...

Page 1018

For example , if a is chosen equal to 0.1 , then ( 2 – alla - 19. Thus , in terms of its

variance , the exponential smoothing method with this value of a is equivalent to

the moving - average method that uses 19 observations . However , if a

For example , if a is chosen equal to 0.1 , then ( 2 – alla - 19. Thus , in terms of its

variance , the exponential smoothing method with this value of a is equivalent to

the moving - average method that uses 19 observations . However , if a

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### 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