## 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 222

( a ) Solve this problem graphically . Identify the CPF solutions by ( a ) Identify the

10 sets of defining equations for this problem . For circling them on the graph .

each one , solve ( if a solution exists ) for the

table ...

( a ) Solve this problem graphically . Identify the CPF solutions by ( a ) Identify the

10 sets of defining equations for this problem . For circling them on the graph .

each one , solve ( if a solution exists ) for the

**corresponding**cor( b ) Develop atable ...

Page 235

Before this goal has been reached , the

slack variables ) of the current tableau must be infeasible for the dual problem .

However , after the goal is reached , the

Before this goal has been reached , the

**corresponding**y in row 0 ( coefficients ofslack variables ) of the current tableau must be infeasible for the dual problem .

However , after the goal is reached , the

**corresponding**y must be an optimal ...Page 252

With the Big M method , since M has been added initially to the coefficient of each

artificial variable in row 0 , the current value of each

is the current coefficient of this artificial variable minus M. For example , look at ...

With the Big M method , since M has been added initially to the coefficient of each

artificial variable in row 0 , the current value of each

**corresponding**dual variableis the current coefficient of this artificial variable minus M. For example , look at ...

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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