## 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 90

Maximize Z = 2xy + 3x2 ( a ) Draw a graph that shows the

Maximize Z = 2xy + 3x2 ( a ) Draw a graph that shows the

**corresponding**objective function lines for Z = 6 , Z = 12 , and Z = 18 . ( b ) Find the slope - intercept form of the equation for each of these three objective function lines .Page 243

5.3 actually identifies its solution for z - c and y as being the

5.3 actually identifies its solution for z - c and y as being the

**corresponding**entries in row 0 . Because of the symmetry property quoted in Sec . 6.1 ( and the direct association between variables shown in Table 6.7 ) ...Page 886

Similarly , the expected total waiting time and the expected number of customers in the entire system can be obtained by merely summing the

Similarly , the expected total waiting time and the expected number of customers in the entire system can be obtained by merely summing the

**corresponding**quantities obtained at the respective facilities . ' For a proof , see P. J. Burke ...### What people are saying - Write a review

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activity additional algorithm allowable amount apply assignment basic solution basic variable BF solution bound boundary called changes coefficients column complete Consider constraints Construct corresponding cost CPF solution decision variables demand described determine distribution dual problem entering equal equations estimates example feasible feasible region FIGURE final flow formulation functional constraints given gives goal identify illustrate increase indicates initial iteration linear programming Maximize million Minimize month needed node nonbasic variables objective function obtained operations optimal optimal solution original parameters path Plant possible presented primal problem Prob procedure profit programming problem provides range remaining resource respective resulting shown shows side simplex method simplex tableau slack solve step supply Table tableau tion unit weeks Wyndor Glass zero