## 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 317

Frederick S. Hillier, Gerald J. Lieberman. Summary of the Parametric Linear

Programming

the problem with ...

Frederick S. Hillier, Gerald J. Lieberman. Summary of the Parametric Linear

Programming

**Procedure**for Systematic Changes in the b ; Parameters . 1. Solvethe problem with ...

Page 369

The

variables one at a time . After each selection , a value that will satisfy one

additional constraint ( thereby eliminating that constraint's row or column from

further ...

The

**procedure**for constructing an initial BF solution selects the m + n - 1 basicvariables one at a time . After each selection , a value that will satisfy one

additional constraint ( thereby eliminating that constraint's row or column from

further ...

Page 628

Applying this

as follows : The constraint is 2x1 + 3x2 = 4 ( a = 2 , az = 3 , b = 4 ) . 1. S = 2 + 3 = 5

. 2. a , satisfies s < b + lail , since 5 < 4 + 2. Also a2 satisfies S < b + | az | , since 5

...

Applying this

**procedure**to the functional constraint in the above example flowsas follows : The constraint is 2x1 + 3x2 = 4 ( a = 2 , az = 3 , b = 4 ) . 1. S = 2 + 3 = 5

. 2. a , satisfies s < b + lail , since 5 < 4 + 2. Also a2 satisfies S < b + | az | , since 5

...

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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