Two parallel insurance lines with simultaneous arrivals and risks correlated with inter-arrival times

We investigate an insurance risk model that consists of two reserves which receive income at fixed rates. Claims are being requested at random epochs from each reserve and the interclaim times are generally distributed. The two reserves are coupled in the sense that at a claim arrival epoch, claims are being requested from both reserves and the amounts requested are correlated. In addition, the claim amounts are correlated with the time elapsed since the previous claim arrival. We focus on the probability that this bivariate reserve process survives indefinitely. The infinite- horizon survival problem is shown to be related to the problem of determining the equilibrium distribution of a random walk with vector-valued increments with reflecting boundary. This reflected random walk is actually the waiting time process in a queueing system dual to the bivariate ruin process. Under assumptions on the arrival process and the claim amounts, and using Wiener-Hopf factor- ization with one parameter, we explicitly determine the Laplace-Stieltjes transform of the survival function, c.q., the two-dimensional equilibrium waiting time distribution. Finally, the bivariate transforms are evaluated for some examples, including for proportional reinsurance, and the bivariate ruin functions are numerically calculated using an efficient inversion scheme.Comment: 24 pages, 6 figure

Inference for double Pareto lognormal queues with applications

In this article we describe a method for carrying out Bayesian inference for the double Pareto lognormal (dPlN) distribution which has recently been proposed as a model for heavy-tailed phenomena. We apply our approach to inference for the dPlN/M/1 and M/dPlN/1 queueing systems. These systems cannot be analyzed using standard techniques due to the fact that the dPlN distribution does not posses a Laplace transform in closed form. This difficulty is overcome using some recent approximations for the Laplace transform for the Pareto/M/1 system. Our procedure is illustrated with applications in internet traffic analysis and risk theory

Inference for double Pareto lognormal queues with applications

In this article we describe a method for carrying out Bayesian inference for the double Pareto lognormal (dPlN) distribution which has recently been proposed as a model for heavy-tailed phenomena. We apply our approach to inference for the dPlN/M/1 and M/dPlN/1 queueing systems. These systems cannot be analyzed using standard techniques due to the fact that the dPlN distribution does not posses a Laplace transform in closed form. This difficulty is overcome using some recent approximations for the Laplace transform for the Pareto/M/1 system. Our procedure is illustrated with applications in internet traffic analysis and risk theory.Heavy tails, Bayesian inference, Queueing theory

Recent Advances in Accumulating Priority Queues

This thesis extends the theory underlying the Accumulating Priority Queue (APQ) in three directions. In the first, we present a multi-class multi-server accumulating priority queue with Poisson arrivals and heterogeneous services. The waiting time distributions for different classes have been derived. A conservation law for systems with heterogeneous servers has been studied. We also investigate an optimization problem to find the optimal level of heterogeneity in the multi-server system. Numerical investigations through simulation are carried out to validate the model. We next focus on a queueing system with Poisson arrivals, generally distributed service times and nonlinear priority accumulation functions. We start with an extension of the power-law APQ in Kleinrock and Finkelstein (1967), and use a general argument to show that there is a linear system of the form discussed in Stanford, Taylor, and Ziedins (2014) which has the same priority ordering of all customers present at any given instant in time, for any sample path. Beyond the power-law case, we subsequently characterize the class of nonlinear accumulating priority queues for which an equivalent linear APQ can be found, in the sense that the waiting time distributions for each of the classes are identical in both the linear and nonlinear systems. Many operational queuing systems must adhere to waiting time targets known as Key Performance Indicators (KPIs), particularly in health care applications. In the last aspect, we address an optimization problem to minimize the weighted average of the expected excess waiting time (WAE), so as to achieve the optimal performance of a system operating under KPIs. We then find that the Accumulating Priority queuing discipline is well suited to systems with KPIs, in that each class of customers progresses fairly towards timely access by its own waiting time limit. Due to the difficulties in minimizing the WAE, we introduce a surrogate objective function, the integrated weighted average excess (IWAE), which provides a useful proxy for WAE. Finally, we propose a rule of thumb in which patients in the various classes accumulate priority credit at a rate that is inversely proportional to their time limits

Propagation of epistemic uncertainty in queueing models with unreliable server using chaos expansions

In this paper, we develop a numerical approach based on Chaos expansions to analyze the sensitivity and the propagation of epistemic uncertainty through a queueing systems with breakdowns. Here, the quantity of interest is the stationary distribution of the model, which is a function of uncertain parameters. Polynomial chaos provide an efficient alternative to more traditional Monte Carlo simulations for modelling the propagation of uncertainty arising from those parameters. Furthermore, Polynomial chaos expansion affords a natural framework for computing Sobol' indices. Such indices give reliable information on the relative importance of each uncertain entry parameters. Numerical results show the benefit of using Polynomial Chaos over standard Monte-Carlo simulations, when considering statistical moments and Sobol' indices as output quantities

The departure process from a GI/G/1 queue and its applications to the analysis of tandem queues

"September 1990."Includes bibliographical references (p. 27-28).Research supported by the Leaders for Manufacturing Program at MIT and the Draper Laboratory.Dimitris J. Bertsimas, Daisuke Nakazato

The departure process from a GI/G/1 queue and its applications to the analysis of tandem queues

"September 1990."Includes bibliographical references (p. 27-28).Research supported by the Leaders for Manufacturing Program at MIT and the Draper Laboratory.Dimitris J. Bertsimas, Daisuke Nakazato

System-theoretical algorithmic solution to waiting times in semi-Markov queues

Cataloged from PDF version of article.Markov renewal processes with matrix-exponential semi-Markov kernels provide a generic tool for modeling auto-correlated interarrival and service times in queueing systems. In this paper, we study the steady-state actual waiting time distribution in an infinite capacity single-server semi-Markov queue with the auto-correlation in interarrival and service times modeled by Markov renewal processes with matrix-exponential kernels. Our approach is based on the equivalence between the waiting time distribution of this semi-Markov queue and the output of a linear feedback interconnection system. The unknown parameters of the latter system need to be determined through the solution of a SDC (Spectral-Divide-and-Conquer) problem for which we propose to use the ordered Schur decomposition. This approach leads us to a completely matrix-analytical algorithm to calculate the steady-state waiting time which has a matrix-exponential distribution. Besides its unifying structure, the proposed algorithm is easy to implement and is computationally efficient and stable. We validate the effectiveness and the generality of the proposed approach through numerical examples. © 2009 Elsevier B.V. All rights reserve

Probability Models for Health Care Operations with Application to Emergency Medicine

This thesis consists of four contributing chapters; two of which are inspired by practical problems related to emergency department (ED) operations management and the remaining two are motivated by the theoretical problem related to the time-dependent priority queue. Unlike classical priority queue, priorities in the time-dependent priority queue depends on the amount of time an arrival waits for service in addition to the priority class they belong. The mismatch between the demand for ED services and the available resources have direct and indirect negative consequences. Moreover, ED physician pay in some jurisdictions reflects pay-for-performance contracts based on operational benchmarks. To assist in capacity planning and meeting these benchmarks, in chapter 4, I built a forecasting model to produce short-term forecasts of ED arrivals. In chapter 5, I empirically investigated the effect of workload on the productivity of ED services. Specifically, under discretionary work setting, different statistical models were fitted to identify the effect of workload and census on four measures of ED service processes, namely, number discharged, length of stay, service time, and waiting time. The time-dependent priority model was first proposed by Kleinrock (1964), and, more recently, naming it accumulating priority queue (APQ), Stanford et al. (2014) derived the waiting time distributions for the various priority classes when the queue has a single server. In chapter 6, I derived expressions for the waiting time distributions for a multi-server APQ with Poisson arrivals for each class, and a common exponential service time distribution. In chapter 7, I worked with a KPI based service system where there are specific time targets by which each class of customers should commence their service and a compliance probability indicating the proportion of customers from that class meeting the target. Recognizing the fact that customer who misses their KPI target is of greater, not lesser importance, I seek to minimize a weighted sum of the expected amount of excess waiting for each class. When minimizing the total expected excess, our numerical examples lead to an easily-implemented rule of thumb for the optimal priority accumulation rates, which can have an immediate impact on health care delivery