## 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 70

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 614

In general terms, two features are sought in choosing a relaxation: it can be

solved relatively quickly, and provides a relatively tight

adequate. The LP relaxation is popular because it provides an excellent trade-off

...

In general terms, two features are sought in choosing a relaxation: it can be

solved relatively quickly, and provides a relatively tight

**bound**. Neither alone isadequate. The LP relaxation is popular because it provides an excellent trade-off

...

Page 640

(a) Design a branch-and-

by specifying how the branch,

b) Use this algorithm to solve this problem. 12.6-9.” Consider the following ...

(a) Design a branch-and-

**bound**algorithm for sequencing problems of this typeby specifying how the branch,

**bound**, and fathoming steps would be performed. (b) Use this algorithm to solve this problem. 12.6-9.” Consider the following ...

### 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 alternative amount analysis apply assignment assumed basic variable begin BF solution calculate called changes coefficients column complete Consider constraints Construct corresponding cost CPF solution customers decision demand described determine developed distribution entering equations estimated example expected feasible FIGURE final flow formulation given gives hour identify illustrate increase indicates initial inventory iteration linear programming machine Maximize mean million Minimize month needed node objective function obtained operations optimal optimal solution original parameter path payoff perform plant player possible presented Prob probability problem procedure profit programming problem queueing respectively resulting shown shows side simplex method solution solve step strategy Table tableau tion transportation unit waiting weeks