## 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 286

functional constraints that has feasible solutions and an unbounded ... Then

solutions .

**Construct**and graph a primal problem with two decision variables and twofunctional constraints that has feasible solutions and an unbounded ... Then

**construct**the dual problem and demonstrate graphically that it has no feasiblesolutions .

Page 288

6.1-4b . subject to ( a )

dual problem graphically . 2x1 + x2 > 2 ( c ) Use the result from part ( b ) to

identify the nonbasic variables and and basic variables for the optimal BF

solution for the ...

6.1-4b . subject to ( a )

**Construct**its dual problem . xy + 2x2 10 ( b ) Solve thisdual problem graphically . 2x1 + x2 > 2 ( c ) Use the result from part ( b ) to

identify the nonbasic variables and and basic variables for the optimal BF

solution for the ...

Page 289

( a )

to determine whether the primal problem has feasible solutions and , if so ,

whether its objective function is bounded . ( a ) Demonstrate graphically that this

...

( a )

**Construct**the dual problem . ( b ) Use graphical analysis of the dual problemto determine whether the primal problem has feasible solutions and , if so ,

whether its objective function is bounded . ( a ) Demonstrate graphically that this

...

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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