In this paper we analyse the optimal claim behaviour of a risk sensitive policy holder having a vehicle damage insurance. It is proved that the optimal decision is of the form: to claim for damages only if its amount exceeds a certain limit. Moreover, we also derive the optimal stopping rule to...

This thesis focuses on increasing the efficiency of systems with cross-trained workforce and finite storage spaces. Our objective is to maximize the throughput and minimize the setup costs (if they exist). More specifically, we are interested in determining effective cross-training strategies...

We propose a mathematical framework that integrates low-level sensory signals from monitoring engineering systems and their components with high-level decision models for maintenance optimization. Our objective is to derive optimal adaptive maintenance strategies that capitalize on condition...

Abstract Assume that the surplus process of an insurance company is described by a general Lévy process and that possible dividend pay-outs to shareholders are restricted to random discrete times which are determined by an independent renewal process. Under this setting we show that the optimal...

In this article we describe reinforcement learning, a machine learning technique for solving sequential decision problems. We describe how reinforcement learning can be combined with function approximation to get approximate solutions for problems with very large state spaces. One such problem...

Game theoretic analysis of queueing systems is an important research direction of queueing theory. In this paper, we study the service rate control problem of closed Jackson networks from a game theoretic perspective. The payoff function consists of a holding cost and an operating cost. Each...

This paper presents a model and solution methodology for scheduling patients in a multi-class, multi-resource surgical system. Specifically, given a master schedule that provides a cyclic breakdown of total OR availability into specific daily allocations to each surgical specialty, the model...

We introduce an approach for semi-parametric inference in dynamic binary choice models that does not impose distributional assumptions on the state variables unobserved by the econometrician. The proposed framework combines Bayesian inference with partial identification results. The method is...

Seeking to reduce the potential impact of delays on radiation therapy cancer patients such as psychological distress, deterioration in quality of life and decreased cancer control and survival, and motivated by inefficiencies in the use of expensive resources, we undertook a study of scheduling...

In this paper we study the problem of personnel planning in care-at-home facilities. We model the system as a Markov decision process, which leads to a high-dimensional control problem. We study monotonicity properties of the system and derive structural results for the optimal policy. Based on...