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The article of record as published may be located at http://dx.doi.org/10.1016/j.ress.2014.04.012 ; Modern systems, civilian (e.g. automotive), and military (manned and unmanned aircraft, surface vehicles, submerged vessels), suffer initial design faults or failure modes (FMs), including software bugs, which detrimentally affect the system's reliability and availability. FMs must be removed or mitigated in impact during initial testing, including accelerated testing, in order for the system to meet its reliability requirements and operate satisfactorily in the field. This paper concerns models for reliability growth in which the behaviors of FMs are assumed independent, but of different types. Test effort is guided by prior information, expressed probabilistically, on the random number and tenacities of such FMs that are of various origins in the designs. Estimation of the numbers of FMs that will ultimately activate while in the field is considered here.

This report provides a way of quickly calculating a measure of what a sensor-equipped platform (e.g., JSTARS or a RPV/UAV) "sees" when it flies across a region containing a large (e.g., brigade-sized) unit. Such observations form the basis for inferring the size and type of the unit, or units. Calculations are in terms of: (1) a convenient parametric model for the spatial arrangement and concentration of an entire unit, the effective size of target items within that unit and their disposition, the sensor's field of view and glance rate, and the speed and course of the sensor-equipped platform's travels across the region containing elements of the large force. Alternatively, calculations are made when; (2) single target items are distributed in any manner in the region, (3) a sensor-bearing platform wanders in accordance with a random walk; and (4) combinations of the above.

Military items such as airborne surveillance systems (UAVs, JSTARS, helicopters, etc.) or combat vehicles (tanks, APCs, ships) may have high effectiveness when available on station, but require occasional restoration (refueling, re-arming, scheduled maintenance) and repair after unscheduled failures of certain subsystems. This requirement takes them off station, where delays occur that are affected by the numbers and types of support resources and the philosophy of scheduling those resources. This paper considers the effect of decision choices on long-run item availability on station when items can be in several levels of capability/effectiveness when on station. The model is used to show that a simple binary decision rule (that depends on ratios of endurance, failure, and restoration and repair rates) guides the decision as to whether a failed item should be completely repaired to its highest level, or returned to duty at an incompletely-capable state. View this as an indicator of the types of rules anticipated to apply in realistic generality. These will be the subject of additional research. ; Defense Operational Test and Evaluation The Pentagon, (Room 3E 318) Washington, DC 20301-1700 ; Defense Operational Test and Evaluation ; MIPR No. DVAM80001

This paper describes the administrative philosophy that currently guides the (evolutionary) acquisition of U.S. military systems. It then sketches a preliminary mathematical model that allows study of the effect of various ways to spend a fixed budget for Block b+1 upgrade so as to obtain a maximum expected number of fielded system upgrades that is effective in the field. This includes the option of simply fielding more of the previous, Block b, design units. Effectiveness/capability growth is the design objective, but testing and fault removal provides for reliability growth. The model accounts for various levels of developmental and testing effort at various rates, and for obsolescence of the previous (Block b) and forthcoming (Block b+1) system versions.

AbstractTechnologically advanced aircraft rely on robust and responsive logistics systems to ensure a high state of operational readiness. This paper fills a critical gap in the literature for combat models by closely relating effectiveness of the logistics system to determinants of success in combat. We present a stochastic diffusion model of an aerial battle between Blue and Red forces. The number of aircraft of Blue forces aloft and ready to be aloft on combat missions is limited by the maximum number of assigned aircraft, the reliability of aircraft subsystems, and the logistic system's ability to repair and replenish those subsystems. Our parsimonious model can illustrate important trade‐offs between logistics decision variables and operational success.

AbstractThis paper describes modeling and operational analysis of a generic asymmetric service‐system situation in which (a) Red agents, potentially threatening, but in another but important interpretation, are isolated friendlies, such as downed pilots, that require assistance and "arrive" according to some partially known and potentially changing pattern in time and space; and (b) Reds have effectively limited unknown deadlines or times of availability for Blue service, i.e., detection, classification, and attack in a military setting or emergency assistance in others. We discuss various service options by Blue service agents and devise several approximations allowing one to compute efficiently those proportions of tasks of different classes that are successfully served or, more generally, if different rewards are associated with different classes of tasks, the percentage of the possible reward gained. We suggest heuristic policies for a Blue server to select the next task to perform and to decide how much time to allocate to that service. We discuss this for a number of specific examples. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006.

Naval Research Logistics, 53 , No. 6, (Sept. 2006), 588-599. ; This paper describes modeling and operational analysis of a generic asymmetric services-system situation in which (a) Red agents, potentially threatening, but in another but important interpretation, are isolated friendlies, such as downed pilots, that require assistance and "arrive" according to some partially known and potentially changing pattern in time and space: and (b) Reds have effectively limited unknown deadlines or times of availability for Blue service, i.e., detection, classification, and attack in a military setting or emergency assistance in others. We discuss various service options by Blue service agents and devise several approximations allowing one to compute efficiently those proportions of tasks of different classes that are successfully serviced, or more generally, if different rewards are associated with different classes of tasks, the percentage of the possible reward gained. We suggest heuristic policies of a Blue server to select the next task to perform and to decide how much time to allocate to that service. We discuss this for a number of specific examples.

A single Red wishes to shoot at a collection of Blue targets in order to maximize some measure of return obtained from Blues killed before Red's own demise. While the class of decision processes called multi-armed bandits has been previously deployed to develop optimal policies for Red, we argue the importance of a little known, but more general class of bandit processes introduced by Nash (1980). In particular, the deployment of this class of processes will enable Red to take account in a natural way of the relative threats posed to his own survival in taking targeting actions. We develop optimal shooting policies for Red in the context of a range of models, which are of independent interest. The paper concludes with a numerical study.

AbstractBlue strike aircraft enter region ℛ to attack Red targets. In Case 1, Blue conducts (preplanned) SEAD to establish air superiority. In the (reactive) SEAD scenario, which is Case 2, such superiority is already in place, but is jeopardized by prohibitive interference from Red, which threatens Blue's ability to conduct missions. We utilize both deterministic and stochastic models to explore optimal tactics for Red in such engagements. Policies are developed which will guide both Red's determination of the modes of operation of his engagement radar, and his choice of Blue opponent to target next. An index in the form of a simple transaction kill ratio plays a major role throughout. Published 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 723–742, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10046