## 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 89

Page 115

The

amount of

this equation , xı 5 4 if and only if 4 - xy = x3 20. Therefore , the original constraint

x1 ...

The

**slack**variable for this constraint is defined to be X3 4 – x1 , which is theamount of

**slack**in the left - hand side of the inequality . Thus , x1 + x3 = 4 . Giventhis equation , xı 5 4 if and only if 4 - xy = x3 20. Therefore , the original constraint

x1 ...

Page 212

The insight involves the coefficients of the

they give . It is a direct result of the initialization , where the ith

is given a coefficient of +1 in Eq . ( i ) and a coefficient of 0 in every other equation

...

The insight involves the coefficients of the

**slack**variables and the informationthey give . It is a direct result of the initialization , where the ith

**slack**variable Xnt ;is given a coefficient of +1 in Eq . ( i ) and a coefficient of 0 in every other equation

...

Page 484

This figure makes it easy to see how much

an activity is the difference between its latest finish time and its earliest finish time

. In symbols ,

This figure makes it easy to see how much

**slack**each activity has . The**slack**foran activity is the difference between its latest finish time and its earliest finish time

. In symbols ,

**Slack**LF – EF . ( Since LF – EF = LS - ES , either difference ...### What people are saying - Write a review

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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