This paper describes a general approach for dynamic control of stochastic networks based
on fluid model analysis, where in broad terms, the stochastic network is approximated by its …

The curse of dimensionality gives rise to prohibitive computational requirements that render
infeasible the exact solution of large-scale stochastic control problems. We study an efficient …

Power grids, flexible manufacturing, cellular communications: interconnectedness has
consequences. This remarkable book gives the tools and philosophy you need to build …

Novel advanced policy gradient (APG) methods, such as trust region policy optimization and
proximal policy optimization (PPO), have become the dominant reinforcement learning …

The existence of an optimal feedback law is established for the risk-sensitive optimal control
problem with denumerable state space. The main assumptions imposed are irreducibility …

The average cost optimal control problem is addressed for Markov decision processes with
unbounded cost. It is found that the policy iteration algorithm generates a sequence of …

In this paper, we consider call admission control of multiple classes without waiting room.
We use event-based dynamic programming for our model. We show that sometimes the …

Within the combinatorial scheduling community, there has been an increasing interest in
modelling and solving scheduling problems in dynamic environments. Such problems have …

Many service systems are staffed by workers who work in shifts. In this article, we study the
dynamic assignment of servers to different areas of a service system at the beginning of …

We provide an introduction to the application of queueing network models to the design and
analysis of semiconductor wafer fabs. We introduce the basic issues that confront the system …