## 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 539

In general, the states are the various

might be at that stage of the problem. The number of states may be either finite (

as in the stagecoach problem) or infinite (as in some subsequent examples).

In general, the states are the various

**possible**conditions in which the systemmight be at that stage of the problem. The number of states may be either finite (

as in the stagecoach problem) or infinite (as in some subsequent examples).

Page 556

... the preceding examples where there were only a few

consider. We now have an infinite number of

it is no longer feasible to solve separately for x: for each

Therefore ...

... the preceding examples where there were only a few

**possible**states toconsider. We now have an infinite number of

**possible**states (240 < s3 = 255), soit is no longer feasible to solve separately for x: for each

**possible**value of s3.Therefore ...

Page 560

It only means that, instead of considering all

variable, we must consider all

state variables. However, from the standpoint of computational efficiency, this

difference ...

It only means that, instead of considering all

**possible**values of the one statevariable, we must consider all

**possible**combinations of values of the severalstate variables. However, from the standpoint of computational efficiency, this

difference ...

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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