## 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 |

### From inside the book

Results 1-3 of 96

Page 216

Even when the simplex method has gone through hundreds or thousands of

iterations , the

how this tableau has been obtained from the initial tableau . Furthermore , the

same ...

Even when the simplex method has gone through hundreds or thousands of

iterations , the

**coefficients**of the slack variables in the final tableau will revealhow this tableau has been obtained from the initial tableau . Furthermore , the

same ...

Page 252

With the Big M method , since M has been added initially to the

artificial variable in row 0 , the current value of each corresponding dual ... After M

is subtracted from the

With the Big M method , since M has been added initially to the

**coefficient**of eachartificial variable in row 0 , the current value of each corresponding dual ... After M

is subtracted from the

**coefficients**of the artificial variables ła and , the optimal ...Page 273

Analyzing Simultaneous Changes in Objective Function

Regardless of whether x ; is a basic or nonbasic variable , the allowable range to

stay optimal for Cj is valid only if this objective function

being ...

Analyzing Simultaneous Changes in Objective Function

**Coefficients**.Regardless of whether x ; is a basic or nonbasic variable , the allowable range to

stay optimal for Cj is valid only if this objective function

**coefficient**is the only onebeing ...

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### Common terms and phrases

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