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 164
Now let us look at the key idea behind Karmarkar's algorithm and its subsequent
variants that use the interior - point approach . The Key Solution Concept
Although radically different from the simplex method , Karmarkar's algorithm does
share ...
Now let us look at the key idea behind Karmarkar's algorithm and its subsequent
variants that use the interior - point approach . The Key Solution Concept
Although radically different from the simplex method , Karmarkar's algorithm does
share ...
Page 166
The two basic factors that determine the performance of an algorithm on a real
problem are the average computer time per iteration and the number of iterations
. Our next comparisons concern these factors . Interior - point algorithms are far ...
The two basic factors that determine the performance of an algorithm on a real
problem are the average computer time per iteration and the number of iterations
. Our next comparisons concern these factors . Interior - point algorithms are far ...
Page 616
easier to fathom with this new fathoming test ( in either form ) , so the algorithm
should run much faster . For a large problem , this acceleration may make the
difference between finishing with a solution guaranteed to be close to optimal
and ...
easier to fathom with this new fathoming test ( in either form ) , so the algorithm
should run much faster . For a large problem , this acceleration may make the
difference between finishing with a solution guaranteed to be close to optimal
and ...
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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