Optimal replacement policies for multistate deteriorating systems

We consider state-age-dependent replacement policies for a multistate deteriorating system. We assume that operating cost rates and replacement costs are both functions of the underlying states. Replacement times and sojourn times in different states are all state-dependent random variables. The optimization criterion is to minimize the expected long-run cost rate. A policy-improvement algorithm to derive the optimal policy is presented. We show that under reasonable assumptions, the optimal replacement policies have monotonic properties. In particular, when the failure-rate functions are nonincreasing, or when all the replacement costs and the expected replacement times are independent of state, we show that the optimal policies are only state dependent. Examples are given to illustrate the structure of the optimal policies in the special case when the sojourntime distributions are Weibull. © 1994 John Wiley & Sons, Inc.     

Book reviews

Military Helicopters. By E. J. Everett‐Heath, G. M. Moss, A. W. Mowat and K. E. Reid. Brassey's, London (1990), ISBN 0–08–037341–0 (hardcover), ISBN 0–08–036716‐X (flexicover), £22.50 (hardcover), £12.95 (flexicover)

The Killing Ground: the British Army, the Western Front and the Emergence of Modern Warfare 1900–1918. By Tim Travers. Unwin‐Hyman, London (1990), ISBN 0–04–4457367, £11.95

The Influence of S. L. A. Marshall on the United States Army. By Major F. D. G. Williams. TRADOC Historical Monograph Series, Fort Monroe, VA (1990)     

Estimating a survival curve when new is better than used with respect to a distinguished set

Hollander, Park, and Proschan define a survival function S of a positive random variable X to be new better than used at age t₀ (NBU-{t₀}) if S satisfies $ \begin{array}{*{20}c} {\frac{{S(x + t_0)}}{{S\left({t_0} \right)}} \le S\left(x \right),} & {{\rm for}\,{\rm all}\,x\, \ge \,0,} \\ \end{array}$ where S(x) = P(X > x). The NBU-{t₀} class is a special case of the NBU-A family of survival distributions, where A is a subset of [0, ∞). These families introduce a variety of modeling possibilities for use in reliability studies. We treat problems of nonparametric estimation of survival functions from these classes by estimators which are themselves members of the classes of interest. For a number of such classes, a recursive estimation technique is shown to produce closed-form estimators which are strongly consistent and converge to the true survival distribution at optimal rates. For other classes, additional assumptions are required to guarantee the consistency of recursive estimators. As an example of the latter case, we demonstrate the consistency of a recursive estimator for S ∈ NBU-[t₀, ∞) based on lifetime data from items surviving a preliminary “burn-in” test. The relative precision of the empirical survival curve and several recursive estimators of S are investigated via simulation; the results provide support for the claim that recursive estimators are superior to the empirical survival curve in restricted nonparametric estimation problems of the type studied here.     

Optimal inspection under semi-markovian deterioration: Basic results

This article develops a model for determining the optimal inspection schedule for a system which deteriorates according to a semi-Markov process that progresses through three states: good, defective, and bad. A binary test is used, and false positives may occur. A true positive results in an action that reduces the likelihood of entering the bad state, but at most one such corrective action can occur during the lifetime of the system. Costs are associated with each inspection, each false positive, the corrective action, and the entrance into the bad state. Dynamic programming is used to compute the minimum expected cost, which is a function of the age of the system. The optimal inspection schedule is readily derived from this value function. Computational examples are provided. This model is appropriate for medical screening or for a mission where there is only one spare part.     

Contrasting the Impacts of Combat and Humanitarian Assistance/Disaster Relief Missions on the Mental Health of Military Service Members

Abstract

We study the differential impacts of combat and humanitarian assistance/disaster relief (HA/DR) missions on the mental health of U.S. Marine Corps members. The deployment experiences of any individual Marine are plausibly random conditional on the observable characteristics which are used to assign Marines into units. Leveraging this exogenous variation, we compare the incidence of post-traumatic stress disorder (PTSD) and suicide deaths among Marines who deployed to either Operation Enduring Freedom/Operation Iraqi Freedom (OEF/OIF) or HA/DR missions between 2001 and 2011. We find that the hazard of PTSD is close to eight times higher among Marines returning from OEF/OIF compared to those never deployed, and just 1.33 times higher among those returning from HA/DR (and never participated in OEF/OIF). Those returning from OEF/OIF missions are 1.81 times more likely than those never deployed to die by suicide when they were still active duty, and the hazard increases to almost 3 after they have left the military. In contrast, we find no difference in the hazards of suicide death between those that deployed to only HA/DR missions and non-deployed Marines.