## 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 129

First , all the tied basic variables reach

variable is increased . Therefore , the one or ones not chosen to be the leaving

basic variable also will have a value of

First , all the tied basic variables reach

**zero**simultaneously as the entering basicvariable is increased . Therefore , the one or ones not chosen to be the leaving

basic variable also will have a value of

**zero**in the new BF solution . ( Note that ...Page 132

+ dinin 2 bi . ever , because a nonbasic variable ( x3 ) then has a

in row 0 , we perform one more iteration in Table 4.10 to identify the other optimal

BF solution . Thus , the two optimal BF solutions are ( 4 , 3 , 0 , 6 , 0 ) and ( 2 , 6 ...

+ dinin 2 bi . ever , because a nonbasic variable ( x3 ) then has a

**zero**coefficientin row 0 , we perform one more iteration in Table 4.10 to identify the other optimal

BF solution . Thus , the two optimal BF solutions are ( 4 , 3 , 0 , 6 , 0 ) and ( 2 , 6 ...

Page 726

However , the focus in this chapter is on the simplest case , called two - person ,

adversaries or players ( who may be armies , teams , firms , and so on ) . They

are called ...

However , the focus in this chapter is on the simplest case , called two - person ,

**zero**- sum games . As the name implies , these games involve only twoadversaries or players ( who may be armies , teams , firms , and so on ) . They

are called ...

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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