## 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

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Page 216

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

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

**coefficients**of the slack variables in the final tableau will reveal how this tableau has been obtained from the initial tableau .Page 252

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

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

**coefficient**of each artificial variable in row 0 , the current value of ... After M is subtracted from the**coefficients**of the artificial variables ła and , the optimal ...Page 273

Analyzing Simultaneous Changes in Objective Function

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 ...### What people are saying - Write a review

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### Common terms and phrases

activity additional algorithm allowable amount apply assignment basic solution basic variable BF solution bound boundary called changes coefficients column complete Consider constraints Construct corresponding cost CPF solution decision variables demand described determine distribution dual problem entering equal equations estimates example feasible feasible region FIGURE final flow formulation functional constraints given gives goal identify illustrate increase indicates initial iteration linear programming Maximize million Minimize month needed node nonbasic variables objective function obtained operations optimal optimal solution original parameters path Plant possible presented primal problem Prob procedure profit programming problem provides range remaining resource respective resulting shown shows side simplex method simplex tableau slack solve step supply Table tableau tion unit weeks Wyndor Glass zero