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 539
In general , the states are the various possible conditions in which the system
might be at that stage of the problem . The number of states may be either finite (
as in the stagecoach problem ) or infinite ( as in some subsequent examples ) .
In general , the states are the various possible conditions in which the system
might be at that stage of the problem . The number of states may be either finite (
as in the stagecoach problem ) or infinite ( as in some subsequent examples ) .
Page 556
We now have an infinite number of possible states ( 240 SS3 < 255 ) , so it is no
longer feasible to solve separately for x * for each possible value of S3 .
Therefore , we instead have solved for x * as a function of the unknown Sz . Using
S3 + ...
We now have an infinite number of possible states ( 240 SS3 < 255 ) , so it is no
longer feasible to solve separately for x * for each possible value of S3 .
Therefore , we instead have solved for x * as a function of the unknown Sz . Using
S3 + ...
Page 560
From a theoretical standpoint , this difference is not particularly serious . It only
means that , instead of considering all possible values of the one state variable ,
we must consider all possible combinations of values of the several state
variables ...
From a theoretical standpoint , this difference is not particularly serious . It only
means that , instead of considering all possible values of the one state variable ,
we must consider all possible combinations of values of the several state
variables ...
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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