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 158
The “ Final Value ” column indicates the optimal solution . The next column gives
the reduced costs . ( We will not discuss these reduced costs now because the
information they provide can also be gleaned from the rest of the upper table . ) ...
The “ Final Value ” column indicates the optimal solution . The next column gives
the reduced costs . ( We will not discuss these reduced costs now because the
information they provide can also be gleaned from the rest of the upper table . ) ...
Page 450
These numbers are summarized in the following table , where a dash indicates
that there is no road directly connecting these two towns without going through
any other towns . The symbols to the left of some of the problems ( or their parts )
...
These numbers are summarized in the following table , where a dash indicates
that there is no road directly connecting these two towns without going through
any other towns . The symbols to the left of some of the problems ( or their parts )
...
Page 765
A decision fork , represented by a square , indicates that a decision needs to be
made at that point in the process . A chance fork , represented by a circle ,
indicates that a random event occurs at that point . Thus , in Fig . 15.9 , the first
decision ...
A decision fork , represented by a square , indicates that a decision needs to be
made at that point in the process . A chance fork , represented by a circle ,
indicates that a random event occurs at that point . Thus , in Fig . 15.9 , the first
decision ...
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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