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 90
Page 267
Analyzing Simultaneous Changes in Right - Hand Sides . When multiple b ;
values are changed simultaneously , the formula b * = S * b can again be used to
see how the righthand sides change in the final tableau . If all these right - hand ...
Analyzing Simultaneous Changes in Right - Hand Sides . When multiple b ;
values are changed simultaneously , the formula b * = S * b can again be used to
see how the righthand sides change in the final tableau . If all these right - hand ...
Page 273
Analyzing Simultaneous Changes in Objective Function Coefficients .
Regardless of whether x ; is a basic or nonbasic variable , the allowable range to
stay optimal for Cj is valid only if this objective function coefficient is the only one
being ...
Analyzing Simultaneous Changes in Objective Function Coefficients .
Regardless of whether x ; is a basic or nonbasic variable , the allowable range to
stay optimal for Cj is valid only if this objective function coefficient is the only one
being ...
Page 316
The set of basic variables in the optimal solution still changes ( as increases )
only where the slope of Z * ( 0 ) changes . However , in contrast to the preceding
case , the values of these variables now change as a ( linear ) function of
between ...
The set of basic variables in the optimal solution still changes ( as increases )
only where the slope of Z * ( 0 ) changes . However , in contrast to the preceding
case , the values of these variables now change as a ( linear ) function of
between ...
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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