## 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

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Page 19

Then , even as personnel changes , the system can be called on at regular intervals to

Then , even as personnel changes , the system can be called on at regular intervals to

**provide**a specific numerical solution . ... 2.4**provides**a good ex- ample of a particularly large computer system for applying a model .Page 81

Thus , the first two types of attributes are input data that will become parameters of the model , whereas the last type ( number of units produced per week of the respective products )

Thus , the first two types of attributes are input data that will become parameters of the model , whereas the last type ( number of units produced per week of the respective products )

**provides**the decision variables for the model .Page 170

We have not discussed re- duced costs in this chapter because the information they

We have not discussed re- duced costs in this chapter because the information they

**provide**can also be gleaned from the al- lowable range to stay optimal for ... When the variable is a nonbasic variable , its reduced cost**provides**some ...### What people are saying - Write a review

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### Other editions - View all

Introduction to Operations Research Frederick S. Hillier,Gerald J. Lieberman No preview available - 2001 |

### Common terms and phrases

activity additional algorithm allowable amount apply assigned basic solution basic variable BF solution bound boundary called changes coefficients column complete Consider Construct corresponding cost CPF solution decision variables described determine developed dual problem entering 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 linear programming model Maximize million Minimize month needed node objective function obtained operations optimal optimal solution original parameters path perform plant possible presented primal problem Prob procedure profit programming problem provides range resource respective resulting revised sensitivity analysis shown shows side simplex method simplex tableau slack solve step Table tableau tion unit values weeks Wyndor Glass x₁ zero