## 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 192

This situation is summarized in Table 5.1, where defining

constraint boundary

any linear programming problem with n decision variables, each CPF solution

lies ...

This situation is summarized in Table 5.1, where defining

**equations**refer to theconstraint boundary

**equations**that yield (define) the indicated CPF solution. Forany linear programming problem with n decision variables, each CPF solution

lies ...

Page 199

Recall that each comer-point solution is the simultaneous solution of a system of

n constraint boundary

question is: How do we tell whether a particular constraint boundary

Recall that each comer-point solution is the simultaneous solution of a system of

n constraint boundary

**equations**, which we called its defining**equations**. The keyquestion is: How do we tell whether a particular constraint boundary

**equation**is ...Page 222

(b) Develop a table giving each of the CPF solutions and the corresponding

defining

these solutions, and use just this information to identify the optimal solution.

(b) Develop a table giving each of the CPF solutions and the corresponding

defining

**equations**, BF solution. and nonbasic variables. Calculate Z for each ofthese solutions, and use just this information to identify the optimal solution.

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### Common terms and phrases

activity additional algorithm amount analysis apply approach assignment assumed basic variable begin BF solution calculate called changes column complete Consider constraints Construct corresponding cost CPF solution customers decision demand described determine developed distribution entering equations estimated example expected feasible FIGURE ﬁrst flow formulation given gives hour identify illustrate increase indicates initial inventory involves iteration linear programming machine Maximize mean million Minimize month needed node objective function obtained operations optimal optimal solution original parameter path 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