Appendix 1:
Poisson distribution:
A Poisson process represents discrete events, like arrival of customers or phone calls at an exchange or a call center. As shown in the Fig. 5 below, N(t) = Number of arrivals in the time interval (0,t) is a random variable, which follows the Poisson distribution.
![Screen Shot 2016-06-08 at 11.49.24 AM](http://old.factordaily.com/wp-content/uploads/2016/06/Screen-Shot-2016-06-08-at-11.49.24-AM.png)
Mathematically, the probability of getting N(t) = x arrivals in a time window (0,t) can be given by,
The inter arrival times are independent and obey an exponential distribution, with mean interarrival time (1/ µ) where µ is the arrival rate per unit time. For example, for an arrival rate of 40 customers per hour, µ =40. Figure 6 demonstrates this graphically.
![Screen Shot 2016-06-08 at 11.52.02 AM](http://old.factordaily.com/wp-content/uploads/2016/06/Screen-Shot-2016-06-08-at-11.52.02-AM.png)
References:
- A. M. Lee and P. A. Longton, “Queueing Processes Associated with Airline Passenger Check-in,” Oper. Res. Quart. 10: 56–71 (1959)
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