## Introduction to Operations ResearchCD-ROM contains: Student version of MPL Modeling System and its solver CPLEX -- MPL tutorial -- Examples from the text modeled in MPL -- Examples from the text modeled in LINGO/LINDO -- Tutorial software -- Excel add-ins: TreePlan, SensIt, RiskSim, and Premium Solver -- Excel spreadsheet formulations and templates. |

### From inside the book

Results 1-3 of 77

Page 318

Therefore , having a large number of upper

functional constraints greatly increases the computational effort required . The

upper

...

Therefore , having a large number of upper

**bound**constraints among thefunctional constraints greatly increases the computational effort required . The

upper

**bound**technique avoids this increased effort by removing the upper**bound**...

Page 615

Finally , note that rather than find an optimal solution , the branch - and -

technique can be used to find a nearly optimal solution , generally with much less

computational effort . For some applications , a solution is “ good enough ” if its Z

...

Finally , note that rather than find an optimal solution , the branch - and -

**bound**technique can be used to find a nearly optimal solution , generally with much less

computational effort . For some applications , a solution is “ good enough ” if its Z

...

Page 1200

... 163 , 320 Best

feasible solution Big M method , 134 - 136 , 139 – 143 , 148 – 149 , 338 , 361 ,

388 Bill of materials , 950 Binary integer programming , 577 applications airline

industry ...

... 163 , 320 Best

**bound**, 609 Beta distribution , 486 BF solution ; see Basicfeasible solution Big M method , 134 - 136 , 139 – 143 , 148 – 149 , 338 , 361 ,

388 Bill of materials , 950 Binary integer programming , 577 applications airline

industry ...

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### Common terms and phrases

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