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Departure characteristics | 12/6 Session Links |
Now that we understand the statistics of arriving customers, let’s look at the departure distribution. The basic difference between these two distributions is that departures depend on arrivals. When the arrival distribution is a Poisson distribution, the statistical distribution that describes their departures is an exponential distribution. It’s the distribution of inter-arrival times of the Poisson distribution. It assumes that customers depart as soon as they’re serviced.
Facilities managers try to manage departures, just as they try to manage arrivals. For example, in supermarkets and in airline check-in areas, there is simply nowhere to stand around after you’ve been serviced. This is a design feature that constrains the customer to leave immediately upon completion of service. Movie theaters actually have special exit doors and crowd control staff to ensure that the theater is emptied as quickly as possible.
Of course, not all managers want to speed up departures — some want to slow them down. For instance, roadside restaurants know that cars (and sometimes tractor-trailers) parked out front are interpreted by prospective customers as endorsements. Speedy service when business is slow is sometimes a counterproductive strategy.
Last Modified: Wednesday, 27-Apr-2016 04:15:26 EDT
Modeling service systems in general is extraordinarily complex, but as we’ve seen, if we make reasonable approximations, we can gain powerful tools that are very easy to apply. In the case of service systems, we assumed that the system was at equilibrium or close to it. Analogously, we can make simplifying assumptions for many other complex questions. Examples are process control, resource scheduling, resource allocation, cost allocation, vehicle routing, and many more.