Inventory sharing under decentralized preventive transshipments

We consider preventive transshipments between two stores in a decentralized system with two demand subperiods. Replenishment orders are made before the first subperiod, and the stores may make transshipments to one another between the subperiods. We prove that the transshipment decision has a dominant strategy, called a control‐band conserving transfer policy, under which each store chooses a quantity to transship in or out that will keep its second‐subperiod starting inventory level within a range called a control band. We prove that the optimal replenishment policy is a threshold policy in which the threshold depends on the capacity level at the other store. Finally, we prove that there does not exist a transfer price that coordinates the decentralized supply chain. Our research also explains many of the differences between preventive and emergency transshipments, including differences in the optimal transfer policies and the existence or nonexistence of transfer prices that coordinate the system. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

The logistic–exponential survival distribution

For various parameter combinations, the logistic–exponential survival distribution belongs to four common classes of survival distributions: increasing failure rate, decreasing failure rate, bathtub‐shaped failure rate, and upside‐down bathtub‐shaped failure rate. Graphical comparison of this new distribution with other common survival distributions is seen in a plot of the skewness versus the coefficient of variation. The distribution can be used as a survival model or as a device to determine the distribution class from which a particular data set is drawn. As the three‐parameter version is less mathematically tractable, our major results concern the two‐parameter version. Boundaries for the maximum likelihood estimators of the parameters are derived in this article. Also, a fixed‐point method to find the maximum likelihood estimators for complete and censored data sets has been developed. The two‐parameter and the three‐parameter versions of the logistic–exponential distribution are applied to two real‐life data sets. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

The rise of targeted killing

Targeted killings have become a central tactic in the United States' campaigns against militant and terrorist groups in the Middle East, Asia, and Africa. Both ‘demand’ and ‘supply’ factors explain the rise of targeted killings. Demand for targeted killings increased as the United States faced new threats from militant groups that could not be effectively countered with conventional military force. Concerns about the political consequences of long-term military involvement overseas and American casualties led political leaders to supply more targeted killings. The conclusion discusses how this tactic may have unintended consequences as other states follow the United States use of targeted killings.     

Estimating the probability of a diffusing target encountering a stationary sensor

The problem addressed is that of estimating the probability of a moving particle (called the target) avoiding detection by a stationary sensor to time t. The target follows a diffusion path and is constrained to remain within a square region R. The detecting sensor is fixed at the center of A. Two expressions for this probability are given. The first results from an approximation to the exact solution of the diffusion equation and the second from experimentation with a Monte Carlo simulation of the diffusion process.     

Learning and costs in airframe production: A multiple-output production function approach

This article examines the influence of both production rate and learning on airframe program costs. A dynamic multiple-output production model is developed and is used to observe the cost impact of changes in production rate and learning. Several simulations are performed to demonstrate the sensitivity of the optimal time path of cost to changes in the model parameters. The model is applied by estimating parameters from the F102 airframe program.     

Book reviews

British Naval Policy and Norwegian Security: Maritime Power in Transition, 1951-60.. By Mats Berdal. Forsvarsstudier No. 2/92, Institutt For Forsvarsstudier (IFS) Oslo, Norway, 1992, ISSN 0333-3981.

The European Security Order Recast: Scenarios for the Post-Cold War Era.. By Barry Buzan, Morten Kelstrup, Pierre Lemaitre, Elzbieta Tromer and OLE Waever. Pinter, London (1990), ISBN 086187143X, £30.00.

Mates and Muchachos: Unit Cohesion in the Falklands War.. By Nora Kinzer Stewart. Brassey's (US), Washington DC (1991), ISBN 008-037-4395, $20.

The Declining Hegemon: The United States and European Defense, 1960-1990.. By Joseph Lepgold. Praeger, New York (1990), ISBN 0-275-93657-0, $15.95 pb.

Inventing Accuracy: A Historical Sociology of Nuclear Missile Guidance.. By Donald Mackenzie. MIT Press, Cambridge MA, and London, England (1990), ISBN 262-132-583, £19.95.     

A Lanchester-type aggregated-force model of conventional ground combat

This article develops a Lanchester-type model of large-scale conventional ground combat between two opposing forces in a “sector”. It is shown that nonlinear Helmbold-type equations of warfare with operational losses may be used to represent the loss-rate curves that have been used in many aggregated-force models. These nonlinear differential equations are used to model the attrition of combat capability (as quantified by a so-called firepower index) in conjunction with a rate-of-advance equation that relates motion of the contact zone (or FEBA) between the opposing forces to the force ratio and tactical decisions of the combatants. This simplified auxiliary model is then used to develop some important insights into the dynamics of FEBA movement used in large-scale aggregated-force models. Different types of behavior for FEBA movement over time are shown to correspond to different ranges of values for the initial force ratio, for example, an attack will “stall out” for a range of initial force ratios above a specific threshold value, but it will “break out” for force ratios above a second specific threshold value. Such FEBA-movement predictions are essentially based on being able to forecast changes over time in the force ratio.