## 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 116

Thus , two of the variables ( called the nonbasic variables ) are set

Thus , two of the variables ( called the nonbasic variables ) are set

**equal**to zero , and then the simultaneous solution of the three ... The number of basic variables**equals**the number of functional constraints ( now equations ) ...Page 241

erates at a strictly positive level ( x ; > 0 ) , the marginal value of the resources it consumes must

erates at a strictly positive level ( x ; > 0 ) , the marginal value of the resources it consumes must

**equal**( as opposed to exceeding ) the unit profit from this activity . The second statement implies that the marginal value of ...Page 487

Intuitively , this formula is placing most of the weight on the most likely estimate and then small

Intuitively , this formula is placing most of the weight on the most likely estimate and then small

**equal**weights on the other two estimates . MS Project provides the option of calculating u for each activity with this formula .### What people are saying - Write a review

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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