## 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 585

A full - fledged

Sec . 12.4 . 12.3 INNOVATIVE USES OF BINARY VARIABLES IN MODEL

decisions of ...

A full - fledged

**formulation**example of this type will be presented at the end ofSec . 12.4 . 12.3 INNOVATIVE USES OF BINARY VARIABLES IN MODEL

**FORMULATION**You have just seen a number of examples where the basicdecisions of ...

Page 591

In general terms , for all the

variables discussed in this section , we need to strike the same note of caution .

This approach sometimes requires adding a relatively large number of such

variables ...

In general terms , for all the

**formulation**possibilities with auxiliary binaryvariables discussed in this section , we need to strike the same note of caution .

This approach sometimes requires adding a relatively large number of such

variables ...

Page 1206

Linear programming - Cont . problem

59 , 60-61 conclusions , 30 graphical solution , 27-30 and OR Courseware , 30–

31 problem

simplex ...

Linear programming - Cont . problem

**formulation**, 45–46 , 48–49 , 52–56 , 58–59 , 60-61 conclusions , 30 graphical solution , 27-30 and OR Courseware , 30–

31 problem

**formulation**, 26–27 and separable programming , 692–696 ofsimplex ...

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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