## 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 |

### From inside the book

Results 1-3 of 67

Page 286

Frederick S. Hillier, Gerald J. Lieberman. í = x220 sic solution for the

Frederick S. Hillier, Gerald J. Lieberman. í = x220 sic solution for the

**dual problem**by using. ( b ) Maximize Z = 2x1 + 5x2 + 3x3 + 4x4 + x5 , 6.1-8 . Consider the following problem . subject to Maximize Z = xy + 2x2 , subject to x1 + ...Page 287

For any linear programming problem in our standard form and its

For any linear programming problem in our standard form and its

**dual problem**, label each of the following statements as true or false and then justify your answer . ( a ) The sum of the number of functional constraints and the number ...Page 288

x220 sic solution for the

x220 sic solution for the

**dual problem**by using Eq . ( 0 ) for the pri- ( a ) How would you identify the optimal solution for the dual mal problem . Then draw your conclusions about whether these problem ? two basic solutions are ...### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Other editions - View all

### 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