## 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 |

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Page 192

This situation is summarized in Table 5.1 , where defining

constraint boundary

For any linear programming problem with n decision variables , each CPF

solution ...

This situation is summarized in Table 5.1 , where defining

**equations**refer to theconstraint boundary

**equations**that yield ( define ) the indicated CPF solution .For any linear programming problem with n decision variables , each CPF

solution ...

Page 199

Recall that each corner - point solution is the simultaneous solution of a system of

n constraint boundary

a particular constraint boundary

Recall that each corner - point solution is the simultaneous solution of a system of

n constraint boundary

**equations**... The key question is : How do we tell whethera particular constraint boundary

**equation**is one of the defining**equations**when ...Page 200

This case corresponds to a CPF solution that satisfies another constraint

boundary

n constraint boundary

the ...

This case corresponds to a CPF solution that satisfies another constraint

boundary

**equation**in addition to its n ... We noted earlier that not every system ofn constraint boundary

**equations**yields a corner - point solution , because eitherthe ...

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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