## 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

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Page 941

checked at discrete intervals , e.g. , at the end of each week , and ordering

decisions are made only at these times even if the

reorder point between the preceding and current review times . ( In practice , a ...

checked at discrete intervals , e.g. , at the end of each week , and ordering

decisions are made only at these times even if the

**inventory**level dips below thereorder point between the preceding and current review times . ( In practice , a ...

Page 951

One form of waste is unnecessary

setup costs , unnecessarily long lead times , production facilities that are not

operational when they are needed , and defective items . Minimizing these forms

of ...

One form of waste is unnecessary

**inventory**. Others are unnecessarily largesetup costs , unnecessarily long lead times , production facilities that are not

operational when they are needed , and defective items . Minimizing these forms

of ...

Page 984

Multiechelon

dramatic shift in

before , the

Multiechelon

**Inventory**Systems Our growing global economy has caused adramatic shift in

**inventory**management entering the 21st century . Now , as neverbefore , the

**inventory**of many manufacturers is scattered throughout the world .### What people are saying - Write a review

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### 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 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