## 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 573

Consider the following integer nonlinear

0.6 0.7 0.8 3 0.8 0.8 0.9 Parallel Units Component 1 Component 2 Component 3

The probability that the system will function is the product of the probabilities that

...

Consider the following integer nonlinear

**programming problem**. 1 0.5 0.6 0.7 20.6 0.7 0.8 3 0.8 0.8 0.9 Parallel Units Component 1 Component 2 Component 3

The probability that the system will function is the product of the probabilities that

...

Page 574

Mmmu z=A+2é subject to >< -~ + R vow lV !° (There are no nonnegativity

constraints.) Use dynamic programming to solve this problem. 11.3-19. Consider

the following nonlinear

x2 5 2.

Mmmu z=A+2é subject to >< -~ + R vow lV !° (There are no nonnegativity

constraints.) Use dynamic programming to solve this problem. 11.3-19. Consider

the following nonlinear

**programming problem**. Maximize Z = xfxz, subject to xj +x2 5 2.

Page 714

Consider the following quadratic

+ 4x; - xg, subject to xl + x2 S 2 and x, 2 O, x; 2 O. (a) Use the KKT conditions to

derive an optimal solution. (b) Now suppose that this problem is to be solved by ...

Consider the following quadratic

**programming problem**: Maximize f(x) = &r| — xf+ 4x; - xg, subject to xl + x2 S 2 and x, 2 O, x; 2 O. (a) Use the KKT conditions to

derive an optimal solution. (b) Now suppose that this problem is to be solved by ...

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activity additional algorithm amount analysis apply approach assignment assumed basic variable begin BF solution calculate called changes column complete Consider constraints Construct corresponding cost CPF solution customers decision demand described determine developed distribution entering equations estimated example expected feasible FIGURE ﬁrst flow formulation given gives hour identify illustrate increase indicates initial inventory involves iteration linear programming machine Maximize mean million Minimize month needed node objective function obtained operations optimal optimal solution original parameter path 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