By J.A. White (Auth.)

Booklet via White, John A., and so on

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**Extra info for Analysis of Queueing Systems**

**Example text**

The probability that the first is at the switchboard is ρ and is (1 — p) for the second. The first operator processes calls in an exponential manner with parameter λ and the second in an exponential manner with parameter λ . The unconditional distribution of processing time per call is then hyperexponential with parameters ρ, λ and X . Suppose, in the above example, that three operators were available for switchboard duty, each processing calls in an exponential fashion but with different parameters, λ λ and λ .

Two events cannot occur simultaneously, 4. no matter how small Τ is, there is a positive probability that an event will occur provided Τ > 0, 5. 25) where λ is the mean number of events occurring in a unit time interval. The Poisson distribution has been successfully used to describe such diverse phenomena as the number of busy channels in a telephone system, the demand for certain products, machine failures, radioactive decay, and customer demand for service. In addition, the Poisson distribution can frequently be used to approximate the binomial distribution.

X = ! (χ - 1)! Let y = χ — 1. e-Ar 2= σ + 2 χ( Α Ώ ! 1. 11 A company places orders for raw materials t weeks prior to scheduled production. The time X taken to receive an order after it has been placed is exponentially distributed with parameter A. If the order arrives prior to production, χ < r, an inventory carrying cost of 1500(r — x) is Probability Theory 45 incurred. If the order arrives after the scheduled production startup, χ > ί, a cost of lost production of 5000(x — t) results. Therefore, C O St _ 11500(i - x\ " |5000(x - i), x