| dc.description.abstract | Queue length distributions provide insight into the impact of service
system design changes that go beyond simple performance measure averages;
however, such distributions are difficult to estimate when service times are not
exponential. In this research, we model service systems using queuing networks
and develop a continuous time Markov chain (CTMC) to compute the steady
state probability distribution function for the number of customers and the
waiting time probabilities in a network of GI/G/c queues. Using a generalised
generator matrix, we evaluate the steady state probability of any number of
customers in the queue. For a network of general queues, we link the queues
using a parametric decomposition approach. Through two service sector
examples, we illustrate that explicitly modelling the arrival and service rates as
general distributions (rather than approximating them using Markovian
distributions) can lead to significantly better resource allocations. | |
