This article studies coherent systems of heterogenous and statistically dependent components' lifetimes. We present a sufficient and necessary condition for a stochastically longer system lifetime resulted by allocating a single active redundancy. For exchangeable components' lifetimes, allocating the redundancy to the component with more minimal path sets is proved to produce a more reliable system, and for systems with stochastic arrangement increasing components' lifetimes and symmetric structure with respect to two components, allocating the redundancy to the weaker one brings forth a larger reliability. Several numerical examples are presented to illustrate the theoretical results as well. © 2016 Wiley Periodicals, Inc. Naval Research Logistics, 2016

]]>Magnetic resonance imaging and other multifunctional diagnostic facilities, which are considered as scarce resources of hospitals, typically provide services to patients with different medical needs. This article examines the admission policies during the appointment management of such facilities. We consider two categories of patients: regular patients who are scheduled in advance through an appointment system and emergency patients with randomly generated demands during the workday that must be served as soon as possible. According to the actual medical needs of patients, regular patients are segmented into multiple classes with different cancelation rates, no-show probabilities, unit value contributions, and average service times. Management makes admission decisions on whether or not to accept a service request from a regular patient during the booking horizon to improve the overall value that could be generated during the workday. The decisions should be made by considering the cancelation and no-show behavior of booked patients as well as the emergency patients that would have to be served because any overtime service would lead to higher costs. We studied the optimal admission decision using a continuous-time discrete-state dynamic programming model. Identifying an optimal policy for this discrete model is analytically intractable and numerically inefficient because the state is multidimensional and infinite. We propose to study a deterministic counterpart of the problem (i.e., the fluid control problem) and to develop a time-based fluid policy that is shown to be asymptotically optimal for large-scale problems. Furthermore, we propose to adopt a mixed fluid policy that is developed based on the information obtained from the fluid control problem. Numerical experiments demonstrate that this improved policy works effectively for small-scale problems. © 2016 Wiley Periodicals, Inc. Naval Research Logistics, 2016

]]>We consider a stochastic partially observable system that can switch between a normal state and a transient abnormal state before entering a persistent abnormal state. Only the persistent abnormal state requires alarms. The transient and persistent abnormal states may be similar in appearance, which can result in excess false alarms. We propose a partially observable Markov decision process model to minimize the false alarm rate, subject to a given upper bound on the expected alarm delay time. The cost parameter is treated as the Lagrange multiplier, which can be estimated from the bound of the alarm delay. We show that the optimal policy has a control-limit structure on the probability of persistent abnormality, and derive closed-form bounds for the control limit and present an algorithm to specify the Lagrange multiplier. We also study a specialized model where the transient and persistent abnormal states have the same observation distribution, in which case an intuitive “watchful-waiting” policy is optimal. © 2016 Wiley Periodicals, Inc.

In this article, we study a class of Quasi-Skipfree (QSF) processes where the transition rate submatrices in the skipfree direction have a column times row structure. Under homogeneity and irreducibility assumptions we show that the stationary distributions of these processes have a product form as a function of the level. For an application, we will discuss the -queue that can be modeled as a QSF process on a two-dimensional state space. In addition, we study the properties of the stationary distribution and derive monotonicity of the mean number of the customers in the queue, their mean sojourn time and the variance as a function of for fixed mean arrival rate. © 2016 Wiley Periodicals, Inc. Naval Research Logistics, 2016

We consider a dynamic pricing model in which the instantaneous rate of the demand arrival process is dependent on not only the current price charged by the concerned firm, but also the present state of the world. While reflecting the current economic condition, the state evolves in a Markovian fashion. This model represents the real-life situation in which the sales season is relatively long compared to the fast pace at which the outside environment changes. We establish the value of being better informed on the state of the world. When reasonable monotonicity conditions are met, we show that better present economic conditions will lead to higher prices. Our computational study is partially calibrated with real data. It demonstrates that the benefit of heeding varying economic conditions is on par with the value of embracing randomness in the demand process. © 2015 Wiley Periodicals, Inc. Naval Research Logistics, 2015

Motivated by applications to service systems, we develop simple engineering approximation formulas for the steady-state performance of heavily loaded *G*/*GI*/*n*+*GI* multiserver queues, which can have non-Poisson and nonrenewal arrivals and non-exponential service-time and patience-time distributions. The formulas are based on recently established Gaussian many-server heavy-traffic limits in the efficiency-driven (ED) regime, where the traffic intensity is fixed at *ρ* > 1, but the approximations also apply to systems in the quality-and-ED regime, where *ρ* > 1 but *ρ* is close to 1. Good performance across a wide range of parameters is obtained by making heuristic refinements, the main one being truncation of the queue length and waiting time approximations to nonnegative values. Simulation experiments show that the proposed approximations are effective for large-scale queuing systems for a significant range of the traffic intensity *ρ* and the abandonment rate *θ*, roughly for *ρ* > 1.02 and *θ* > 2.0. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 187–217, 2016

This article studies the optimal capacity investment problem for a risk-averse decision maker. The capacity can be either purchased or salvaged, whereas both involve a fixed cost and a proportional cost/revenue. We incorporate risk preference and use a consumption model to capture the decision maker's risk sensitivity in a multiperiod capacity investment model. We show that, in each period, capacity and consumption decisions can be separately determined. In addition, we characterize the structure of the optimal capacity strategy. When the parameters are stationary, we present certain conditions under which the optimal capacity strategy could be easily characterized by a static two-sided (*s*, *S*) policy, whereby, the capacity is determined only at the beginning of period one, and held constant during the entire planning horizon. It is purchased up to *B* when the initial capacity is below *b*, salvaged down to Σ when it is above σ, and remains constant otherwise. Numerical tests are presented to investigate the impact of demand volatility on the optimal capacity strategy. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 218–235, 2016

We consider the problem of determining the capacity to assign to each arc in a given network, subject to uncertainty in the supply and/or demand of each node. This design problem underlies many real-world applications, such as the design of power transmission and telecommunications networks. We first consider the case where a set of supply/demand scenarios are provided, and we must determine the minimum-cost set of arc capacities such that a feasible flow exists for each scenario. We briefly review existing theoretical approaches to solving this problem and explore implementation strategies to reduce run times. With this as a foundation, our primary focus is on a chance-constrained version of the problem in which *α*% of the scenarios must be feasible under the chosen capacity, where *α* is a user-defined parameter and the specific scenarios to be satisfied are not predetermined. We describe an algorithm which utilizes a separation routine for identifying violated cut-sets which can solve the problem to optimality, and we present computational results. We also present a novel greedy algorithm, our primary contribution, which can be used to solve for a high quality heuristic solution. We present computational analysis to evaluate the performance of our proposed approaches. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 236–246, 2016

We present a validation of a centralized feedback control law for robotic or partially robotic water craft whose task is to defend a harbor from an intruding fleet of water craft. Our work was motivated by the need to provide harbor defenses against hostile, possibly suicidal intruders, preferably using unmanned craft to limit potential casualties. Our feedback control law is a sample-data receding horizon control law, which requires the solution of a complex max-min problem at the start of each sample time. In developing this control law, we had to deal with three challenges. The first was to develop a max-min problem that captures realistically the nature of the defense-intrusion game. The second was to ensure the solution of this max-min problem can be accomplished in a small fraction of the sample time that would be needed to control a possibly fast moving craft. The third, to which this article is dedicated, was to validate the effectiveness of our control law first through computer simulations pitting a computer against a computer or a computer against a human, then through the use of model hovercraft in a laboratory, and finally on the Chesapeake Bay, using Yard Patrol boats. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 247–259, 2016

The theory of directed graphs and noncooperative games is applied to the problem of verification of State compliance to international treaties on arms control, disarmament and nonproliferation of weapons of mass destruction. Hypothetical treaty violations are formulated in terms of illegal *acquisition paths* for the accumulation of clandestine weapons, weapons-grade materials or some other military capability. The paths constitute the illegal strategies of a sovereign State in a two-person *inspection game* played against a multi- or international Inspectorate charged with compliance verification. The effectiveness of existing or postulated verification measures is quantified in terms of the Inspectorate's expected utility at Nash equilibrium. A prototype software implementation of the methodology and a case study are presented. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 260–271, 2016