In this talk I'll discuss 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 leave the system. Specifically, every customer has a patience threshold, and they balk if the observed delay is above the threshold. The main objective is to estimate the parameters of 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, and remarkably, we manage to devise a procedure to estimate the underlying patience and arrival rate parameters. The underlying model is a multiserver queue with a Poisson stream of customers, enabling evaluation of the corresponding likelihood function of the state-dependent effective arrival process. We establish strong consistency of the MLE and derive the asymptotic distribution of the estimation error. Several applications and extensions of the method are discussed. The performance is further assessed through a series of numerical experiments. By fitting parameters of hyper-exponential and generalized hyperexponential distributions, our method provides a robust estimation framework for any continuous patience-level distribution. The last part of the talk will discuss the setting in which the arrival process is not constant but follows a periodic pattern (say, following a daily pattern) -- in this setup various technical hurdles have to be overcome, primarily related to establishing an appropriate regeneration structure. This is joint work with Yoshiaki Inoue, Liron Ravner, and Shreehari Bodas.
Friday, 10 November 2023, 4:00 pm
Hybrid, Anita B Lawrence (H13) East 4082