## 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 237

This x is feasible for the primal

solutions property also holds at the final iteration of the simplex method, where

an optimal ...

This x is feasible for the primal

**problem**, but this y is not feasible for the dual**problem**(since it violates the constraint, y1 + 3y2 = 3). The complementarysolutions property also holds at the final iteration of the simplex method, where

an optimal ...

Page 287

For any linear programming

label each of the following statements as ... constraints and the number of

variables (before augmenting) is the same for both the primal and the dual

For any linear programming

**problem**in our standard form and its dual**problem**,label each of the following statements as ... constraints and the number of

variables (before augmenting) is the same for both the primal and the dual

**problems**.Page 288

sic solution for the dual

draw your conclusions about whether these two basic solutions are optimal for

their respective

solution ...

sic solution for the dual

**problem**by using Eq. (0) for the primal**problem**. Thendraw your conclusions about whether these two basic solutions are optimal for

their respective

**problems**. I (d) Solve the dual**problem**graphically. Use thissolution ...

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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 perform 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