This paper provides series expansions of the stationary distribution of a finite Markov chain. This leads to an efficient numerical algorithm for computing the stationary distribution of a finite Markov chain. Numerical examples are given to illustrate the performance of the algorithm.

This paper addresses the problem of approximately computing the Lyapunov exponent of stochastic max-plus linear systems. Our approach allows for an efficient simulation of bounds for the Lyapunov exponent. We provide sufficient conditions for the convergence of the bounds. In particular, a...

In this paper we study a challenging call center operation problem. The goal of our analysis is to identify an optimal policy for allocating tasks to agents. As a first step, we discuss promising randomized policies and use stochastic approximation for finding the optimal randomized policy when...

Structural properties of generalised semi-Markov processes (GSMP) have been successfully studied in the literature. Examples are the celebrated commuting condition (CC), which is the key condition for unbiasedness of the infinitesimal perturbation analysis (IPA) gradient estimator, or the...

This paper deals with a system where batch arrivals wait in a station until a server (a train) is available, at which moment it services all customers in waiting. This is an example of a bulk server, which has many applications in public transportation, telecommunications, computer resource...

This paper provides series expansions of the stationary distribution of a finite Markov chain. This leads to an efficient numerical algorithm for computing the stationary distribution of a finite Markov chain. Numerical examples are given to illustrate the performance of the algorithm.

Quantiles play an important role in modelling quality of service in the service industry and in modelling risk in the financial industry. Recently, Hong showed in his breakthrough papers that efficient simulation based estimators can be obtained for quantile sensitivities by means of sample path...