5 research outputs found
Estimating customer impatience in a service system with unobserved balking
This paper studies a service system in which arriving customers are provided
with information about the delay they will experience. Based on this
information they decide to wait for service or to leave the system. The main
objective is to estimate the customers' patience-level distribution and the
corresponding potential arrival rate, using knowledge of the actual
queue-length process only. The main complication, and distinguishing feature of
our setup, lies in the fact that customers who decide not to join are not
observed, but, remarkably, we manage to devise a procedure to estimate the load
they would generate. We express our system in terms of a multi-server queue
with a Poisson stream of customers, which allows us to evaluate the
corresponding likelihood function. Estimating the unknown parameters relying on
a maximum likelihood procedure, we prove strong consistency and derive the
asymptotic distribution of the estimation error. Several applications and
extensions of the method are discussed. The performance of our approach is
further assessed through a series of numerical experiments. By fitting
parameters of hyperexponential and generalized-hyperexponential distributions
our method provides a robust estimation framework for any continuous
patience-level distribution
Asymptotic inference for waiting times and patiences in queues with abandonment
International audienceMotivated by applications in call center management, we propose a framework based on empirical process techniques for inference about waiting time and patience distributions in multiserver queues with abandonment. The framework rigorises heuristics based on survival analysis of independent and identically distributed observations by allowing correlated waiting times. Assuming a regenerative structure of offered waiting times, we establish asymptotic properties of estimators of limiting distribution functions and derived functionals. We discuss construction of bootstrap confidence intervals and statistical tests, including a simple bootstrap two-sample test for comparing patience distributions. A small simulation study and a real data example are presented