## 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 170

The column to the right of these values

discussed reduced costs in this chapter because the information they provide can

also be gleaned from the allowable range to stay optimal for the coefficients in

the ...

The column to the right of these values

**gives**the reduced costs . We have notdiscussed reduced costs in this chapter because the information they provide can

also be gleaned from the allowable range to stay optimal for the coefficients in

the ...

Page 231

For the primal problem , each column ( except the Right Side column )

coefficients of a single variable in the respective constraints and then in the

objective function , whereas each row ( except the bottom one )

parameters ...

For the primal problem , each column ( except the Right Side column )

**gives**thecoefficients of a single variable in the respective constraints and then in the

objective function , whereas each row ( except the bottom one )

**gives**theparameters ...

Page 1107

22.5 is convenient if the simulation is done manually , the computer must revert to

some alternative approach . For discrete distributions , a table lookup approach

can be taken by constructing a table that

...

22.5 is convenient if the simulation is done manually , the computer must revert to

some alternative approach . For discrete distributions , a table lookup approach

can be taken by constructing a table that

**gives**a “ range " ( jump ) in the value of...

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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