A combined optimal dividend/reinsurance problem with two types of insurance claims, namely the expected premium principle and the variance premium principle, is discussed. Dividend payments are considered with both fixed and proportional transaction costs. The objective of an insurer is to determine an optimal dividend–reinsurance policy so as to maximize the expected total value of discounted dividend payments to shareholders up to ruin time. The problem is formulated as an optimal regular-impulse control problem. Closed-form solutions for the value function and optimal dividend–reinsurance strategy are obtained in some particular cases. Finally, some numerical analysis is given to illustrate the effects of safety loading on optimal reinsurance strategy.

We consider the problem of optimally replacing multiple stochastically degrading systems using condition-based maintenance. Each system degrades continuously at a rate that is governed by the current state of the environment, and each fails once its own cumulative degradation threshold is reached. The objective is to minimize the sum of the expected total discounted setup, preventive replacement, reactive replacement, and downtime costs over an infinite horizon. For each environment state, we prove that the cost function is monotone nondecreasing in the cumulative degradation level. Additionally, under mild conditions, these monotonicity results are extended to the entire state space. In the case of a single system, we establish that monotone policies are optimal. The monotonicity results help facilitate a tractable, approximate model with state- and action-space transformations and a basis-function approximation of the action-value function. Our computational study demonstrates that high-quality, near-optimal policies are attainable and significantly outperform heuristic policies.

Bounds of the quantile entropy in the past lifetime of some ageing classes are explored firstly. The quantile entropy in the past lifetime of a random variable is shown to be increasing if its expected inactivity time is increasing. Some closure properties of the less quantile entropy in the past lifetime order are obtained under the model of generalized order statistics. Moreover, sufficient conditions are given for a function of a random variable and for a weighted random variable to have more quantile entropy in the past lifetime than original random variable.

The joint signatures of binary-state and multi-state (semi-coherent or mixed) systems with i.i.d. (independent and identically distributed) binary-state components are considered in this work. For the comparison of pairs of binary-state systems of different sizes, transformation formulas of their joint signatures are derived by using the concept of equivalent systems and a generalized triangle rule for order statistics. Similarly, for facilitating the comparison of pairs of multi-state systems of different sizes, transformation formulas of their multi-state joint signatures are also derived. Some examples are finally presented to illustrate and to verify the theoretical results established here.

In this paper, we study three delay systems where different classes of impatient customers arrive according to independent Poisson processes. In the first system, a single server receives two classes of customers with general service time requirements, and follows a non-preemptive priority policy in serving them. Both classes of customers abandon the system when their exponentially distributed patience limits expire. The second system comprises parallel and identical servers providing the same type of service for both classes of impatient customers under the non-preemptive priority policy. We assume exponential service times and consider two cases depending on the time-to-abandon distribution being exponentially distributed or deterministic. In either case, we permit different reneging rates or patience limits for each class. Finally, we consider the first-come-first-served policy in single- and multi-server settings. In all models, we obtain the Laplace transform of the virtual waiting time for each class by exploiting the level-crossing method. This enables us to compute the steady-state system performance measures.

This paper introduces a special issue of this journal (Probability in the Engineering and Informational Sciences) that is devoted to G(elenbe)-Networks and their Applications. The special issue is based on revised versions of some of the papers that were presented at a workshop held in early January 2017 at the Séminaire Saint-Paul in Nice (France). It includes contributions in several research directions that followed from the introduction of the G-Network in the late 1980s. The papers present original theoretical developments, as well as applications of G-Networks to Machine Learning, to the performance optimization of energy systems via the novel Energy Packet Networks formalism for systems that operate with renewable and intermittent energy sources, and to packet network routing and Cloud management over the Internet. We introduce these contributions from the perspective of an overview of recent work based on G-Networks.

We are concerned with a burst arrival single-server queue, where arrivals of cells in a burst are synchronized with a constant service time. The main concern is with the loss probability of cells for the queue with a finite buffer. We analyze an embedded Markov chain at departure instants of cells and get a kind of lumpability for its state space. Based on these results, this paper proposes a computation algorithm for its stationary distribution and the loss probability. Closed formulas are obtained for the first two moments of the numbers of cells and active bursts when the buffer size is infinite.

The total time on test transform (ttt) order was introduced in Kochar, Li, and Shaked (2002) for nonnegative random variables, which has a close connection to the location independent riskier and the excess wealth orders. In this article, the ttt order is redefined for comparing not necessarily nonnegative random variables. Such an extension will lead us to conveniently derive several new intrinsic properties of the ttt order. In particular, we establish an interesting separation result on the connection between the ttt and the excess wealth orders. As two applications of this separation result, we obtain the generating process of the ttt order in terms of mean-decreasing right stretches and the closure property of the ttt order under convolutions. The generating process of the ttt order parallels those of the location independent riskier and the excess wealth orders established by Landsberger and Meilijson (1994).

The concept of generalized order statistics was introduced as a unified approach to a variety of models of ordered random variables. The purpose of this article is to establish several stochastic comparisons of simple spacings in the mean residual life and the excess wealth orders under the more general assumptions on the parameters of the models.

In this article we develop techniques for applying Approximate Dynamic Programming (ADP) to the control of time-varying queuing systems. First, we show that the classical state space representation in queuing systems leads to approximations that can be significantly improved by increasing the dimensionality of the state space by state disaggregation. Second, we deal with time-varying parameters by adding them to the state space with an ADP parameterization. We demonstrate these techniques for the optimal admission control in a retrial queue with abandonments and time-varying parameters. The numerical experiments show that our techniques have near to optimal performance.

Let X1,X2,… be an ergodic Markov chain on the countable state space. We construct a strong stationary dual chain X* whose first hitting times give sharp bounds on the convergence to stationarity for X. Examples include birth and death chains, queueing models, and the excess life process of renewal theory. This paper gives the first extension of the stopping time arguments of Aldous and Diaconis [1,2] to infinite state spaces.

We consider the impact that the introduction of dependent component failures has on the computational difficulty of system reliability analysis. We develop general strategies for computing system reliability when components are subject to dependent failures. In the process, we identify a significant number of cases where computing the system reliability in the presence of dependent failures is not significantly harder than writing down the joint probability distribution and computing the system reliability when the components fail independently.

We examine how overflow policies in a multi-skill call center should be designed to accommodate performance measures that depend on waiting time percentiles such as service level. This is done using a discrete Markovian approximation of the waiting time of the first customers waiting in line. A Markov decision chain is used to determine the optimal policy. This policy outperforms considerably the ones used most often in practice, which use a fixed threshold. The present method can be used also for other call-center models and other situations where performance is based on actual waiting times and customers are treated in a FCFS order.

We consider the problem of finding the optimal routing of a single vehicle that delivers K different products to N customers that are served according to a particular order. It is assumed that the demands of the customers for each product are discrete random variables, and the total demand of each customer for all products cannot exceed the vehicle capacity. The joint probability mass function of the demands of each customer is known. It is assumed that all products are stored together in the vehicle's single compartment. The policy that serves all customers with the minimum total expected cost is found by implementing a suitable dynamic programming algorithm. We prove that this policy has a specific threshold-type structure. Furthermore, we study a corresponding infinite-time horizon problem in which the service of the customers is not completed when the last customer has been serviced but it continues periodically with the same customer order. The demands of each customer for the products have the same distributions at different periods. The discounted cost optimal policy and the average-cost optimal policy have the same structure as the optimal policy in the finite-horizon problem. Numerical results are given that illustrate the structural results.

Software reliability is one of important characteristics of software quality, and software release time is an important application of the software reliability model. In this article we consider a software release policy based on a Gamma-Gamma-type Kalman filter as well as the risk cost due to software failures and the cost for debugging in software systems. Under this model, the optimal release time that minimizes the expected cost in every test-debugging stage subject to a reliability constraint is discussed. An example to illustrate the framework of our model is given.

In this paper, we study an M/M/1 queue, where the server continues to work during idle periods and builds up inventory. This inventory is used for new arriving service requirements, but it is completely emptied at random epochs of a non-homogeneous Poisson process, whose rate depends on the current level of the acquired inventory. For several shapes of depletion rates, we derive differential equations for the stationary density of the workload and the inventory level and solve them explicitly. Finally, numerical illustrations are given for some particular examples, and the effects of this depletion mechanism are discussed.

We consider a polling system where a group of an infinite number of servers visits sequentially a set of queues. When visited, each queue is attended for a random time. Arrivals at each queue follow a Poisson process, and the service time of each individual customer is drawn from a general probability distribution function. Thus, each of the queues comprising the system is, in isolation, an M/G/∞-type queue. A job that is not completed during a visit will have a new service-time requirement sampled from the service-time distribution of the corresponding queue. To the best of our knowledge, this article is the first in which an M/G/∞-type polling system is analyzed. For this polling model, we derive the probability generating function and expected value of the queue lengths and the Laplace–Stieltjes transform and expected value of the sojourn time of a customer. Moreover, we identify the policy that maximizes the throughput of the system per cycle and conclude that under the Hamiltonian-tour approach, the optimal visiting order is independent of the number of customers present at the various queues at the start of the cycle.

This paper models primary computer storage in the context of a general (GI/GI/l) queueing system. Queued items are described by sizes, or storage requirements, as well as by arrival and service times; the sum of the sizes of the items in the system is the occupied storage. Capacity constraints are represented by two different protocols for determining whether an arriving item is admitted to the system: (1) an item is accepted if and only if at its arrival time the currently occupied storage does not exceed a given constant C > 0, and (2) an item is accepted if and only if at its arrival time the occupied storage is at most C, and the occupied storage plus the item's size is at most C(l + ε) for some given ε > 0. We prove for both systems that in heavy traffic the occupied storage, suitably normalized, converges weakly to reflected Brownian motion with boundaries at 0 and at C. A distinctive feature of the proof is the characterization of reflected Brownian motion as a limit of unrestricted penalized processes.

These results make more plausible an earlier conjecture of the authors, i.e., that one obtains the same heavy traffic limit when the admission rule is: accept an item if and only if at its arrival time the occupied storage plus the item's size is no greater than C.

In this paper, we consider a financial market with an asset exposed to a risk inducing a jump in the asset price, and which can still be traded after the default time. We use a default-intensity modeling approach, and address in this incomplete market context the problem of maximization of expected utility from terminal wealth for logarithmic, power and exponential utility functions. We study this problem as a stochastic control problem both under full and partial information. Our contribution consists of showing that the optimal strategy can be obtained by a direct approach for the logarithmic utility function, and the value function for the power (resp. exponential) utility function can be determined as the minimal (resp. maximal) solution of a backward stochastic differential equation. For the partial information case, we show how the problem can be divided into two problems: a filtering problem and an optimization problem. We also study the indifference pricing approach to evaluate the price of a contingent claim in an incomplete market and the information price for an agent with insider information.

In this paper, the complete consistency for the weighted estimator of non-parametric regression model based on widely orthant-dependent errors is established, where the restriction imposed on the dominating coefficient g(n) is very general. Moreover, under some stronger moment condition, we further obtain the convergence rate of the complete consistency, where the assumption on the dominating coefficient g(n) is also very general.