## 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 176

4.4-4 for the following problem . and

. subject to x ] + 2x2 < 30 X1 + X2 = 20 and DI ( a ) Work through the simplex

method step by step in algebraic form to solve this problem . DI ( b ) Work through

...

4.4-4 for the following problem . and

**Maximize**Z = 2xy + 3x2 , x 20 , X220 , X3 20. subject to x ] + 2x2 < 30 X1 + X2 = 20 and DI ( a ) Work through the simplex

method step by step in algebraic form to solve this problem . DI ( b ) Work through

...

Page 573

Component 4 1 subject to 2 3 0.5 0.6 0.8 0.6 0.7 0.8 0.7 0.8 0.9 0.5 0.7 0.9 x1 +

2x2 + 3x3 = 10 x 21 , X2 21 , xz 21 , The probability that the system will function is

...

**Maximize**Z = xzxzx } , Parallel Units Component 1 Component 2 Component 3Component 4 1 subject to 2 3 0.5 0.6 0.8 0.6 0.7 0.8 0.7 0.8 0.9 0.5 0.7 0.9 x1 +

2x2 + 3x3 = 10 x 21 , X2 21 , xz 21 , The probability that the system will function is

...

Page 574

Consider the following nonlinear programming problem . x } + x 2 2 . ( There are

no nonnegativity constraints . ) Use dynamic programming to solve this problem .

...

Consider the following nonlinear programming problem . x } + x 2 2 . ( There are

no nonnegativity constraints . ) Use dynamic programming to solve this problem .

**Maximize**Z = 5x1 + x2 , 11.3-19 . Consider the following nonlinear programming...

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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