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 225
You are given the facts that the basic variables in the optimal solution are X3 , X1
, and xs and that 3 1 0 11 -3 1 2 4 6 9 -3 27 0 3 2 -3 10 Let X4 , X5 , and x6
denote the slack variables for the respective constraints . After you apply the
simplex ...
You are given the facts that the basic variables in the optimal solution are X3 , X1
, and xs and that 3 1 0 11 -3 1 2 4 6 9 -3 27 0 3 2 -3 10 Let X4 , X5 , and x6
denote the slack variables for the respective constraints . After you apply the
simplex ...
Page 713
4.6 and then applying the KKT conditions as given in Sec . 13.6 . ) 13.6-13 .
Consider the following nonlinear programming problem : Minimize Z = 2x + x3 , (
a ) Obtain the KKT conditions for this problem . ( b ) Use the KKT conditions to
check ...
4.6 and then applying the KKT conditions as given in Sec . 13.6 . ) 13.6-13 .
Consider the following nonlinear programming problem : Minimize Z = 2x + x3 , (
a ) Obtain the KKT conditions for this problem . ( b ) Use the KKT conditions to
check ...
Page 1187
... ( statelfinding ) 0.45 win and win 0.818 win , given win 0.75 win , given win lose
, given win 0.25 0.333 0.6 0.15 win and lose win , given lose Win Lose 0.4 0.1
lose and win 0.182 lose , given win 0.25 win , given lose lose , given lose 0.75
0.3 ...
... ( statelfinding ) 0.45 win and win 0.818 win , given win 0.75 win , given win lose
, given win 0.25 0.333 0.6 0.15 win and lose win , given lose Win Lose 0.4 0.1
lose and win 0.182 lose , given win 0.25 win , given lose lose , given lose 0.75
0.3 ...
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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