## 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 318

Therefore , having a large number of upper

Therefore , having a large number of upper

**bound**constraints among the functional constraints greatly increases the computational effort required . The upper**bound**technique avoids this increased effort by removing the upper**bound**...Page 319

The upper

The upper

**bound**technique uses the following rule to make this choice : Rule : Begin with choice 1 . Whenever x ; = 0 , use choice 1 , so x ; is nonbasic . Whenever x ; = Uj , use choice 2 , so y ; - O is nonbasic .Page 614

In general terms , two features are sought in choosing a relaxation : it can be solved relatively quickly , and provides a relatively tight

In general terms , two features are sought in choosing a relaxation : it can be solved relatively quickly , and provides a relatively tight

**bound**. Neither alone is adequate . The LP relaxation is popular because it provides an ...### What people are saying - Write a review

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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