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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Results 1-3 of 68
Page 40
The conclusion was that proportionality could indeed be assumed without
serious distortion . For other problems , what happens when the proportionality
assumption does not hold even as a reasonable approximation ? In most cases ,
this ...
The conclusion was that proportionality could indeed be assumed without
serious distortion . For other problems , what happens when the proportionality
assumption does not hold even as a reasonable approximation ? In most cases ,
this ...
Page 42
For other problems , if additivity is not a reasonable assumption , so that some of
or all the mathematical functions of the ... Since each decision variable
represents the level of some activity , it is being assumed that the activities can be
run at ...
For other problems , if additivity is not a reasonable assumption , so that some of
or all the mathematical functions of the ... Since each decision variable
represents the level of some activity , it is being assumed that the activities can be
run at ...
Page 43
Certainty Our last assumption concerns the parameters of the model , namely ,
the coefficients in the objective function cj ... bir Certainty assumption : The value
assigned to each parameter of a linear programming model is assumed to be a ...
Certainty Our last assumption concerns the parameters of the model , namely ,
the coefficients in the objective function cj ... bir Certainty assumption : The value
assigned to each parameter of a linear programming model is assumed to be a ...
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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