Minimizing reductions in readiness caused by time-phased decreases in aircraft overhaul and repair activities

The Navy is often required to conduct personnel planning studies, A recent study on the assessment of effects of personnel reductions at the various aircraft Overhaul and Repair Activities (O & Rs) of the Bureau of Naval Weapons was carried out by one of the writers of the present paper. The methodology was systematic but relied on informal, judgmental decision procedures rather than on formal models incorporating optimization. Later study of the problem resulted in the approach described herein. The paper addresses this question: How can a personnel reduction in the Navy's aircraft O & Rs be distributed among activities with minimum reduction in readiness? A two-stage procedure involving linear programming models is developed. Solutions involve either extensions of aircraft overhaul cycles or a combination of such extensions with reductions in aircraft inventories.     

Optimum sequential search with discrete locations and random acceptance errors

Much work has been done in search theory; however, very little effort has occurred where an object's presence at a location can be accepted when no object is present there. The case analyzed is of this type. The number of locations is finite, a single object is stationary at one location, and only one location is observed each step of the search. The object's location has a known prior probability distribution. Also known are the conditional probability of acceptance given the object's absence (small) and the conditional probability of rejection given the object's presence (not too large); these Probabilities remain fixed for all searching and locations. The class of sequential search policies which terminate the search at the first acceptance is assumed. A single two-part optimization criterion is considered. The search sequence is found which (i) minimizes the probability of obtaining n rejections in the first n steps for all n, and (ii) maximizes the probability that the first acceptance occurs within the first n steps and occurs at the object's location for all n. The optimum sequential search policy specifies that the next location observed is one with the largest posterior probability of the object's presence (evaluated after each step from Bayes Rule) and that the object is at the first location where acceptance occurs. Placement at the first acceptance seems appropriate when the conditional probability of acceptance given the object's absence is sufficiently small. Search always terminates (with probability one). Optimum truncated sequential policies are also considered. Methods are given for evaluating some pertinent properties and for investigating the possibility that no object occurs at any location.     

THE CONCEPT OF NUCLEAR LEARNING

This article seeks to elucidate the concept of nuclear learning. It explores both the “nuclear” and the “learning” aspects of the concept. On the nuclear side, it distinguishes between learning basic facts about nuclear arms and drawing inferences about the larger implications of those facts. On the learning side, it discusses three issues: whether to use the term in a normative or value-neutral manner; the difference between learning that leads to a change in means versus learning that leads to a re-evaluation of ends; and whether learning only takes place at the level of individuals or whether there can also be learning by collective entities. The article argues there is no universal best answer to these questions and that the particular concept of learning that should be employed depends on the goals of the analyst. If the goal is to reduce the chances of nuclear war, however, one type of learning that will be important to consider is whether there is shared, cross-national learning.     

Bomboozled: How the U.S. Government Misled Itself and Its People into Believing They Could Survive a Nuclear Attack

Bomboozled: How the U.S. Government Misled Itself and Its People into Believing They Could Survive a Nuclear Attack, by Susan Roy. Pointed Leaf Press, 2011. 176 pages, $45.     

What explains the failure of U.S. army reconstruction in Afghanistan?

ABSTRACT

This author wrote his doctorate thesis on post-conflict army reconstruction, submitting it in 2011. Continued research on the subject in the intervening seven years indicates that his theoretical propositions can be refined and improved. This article examines refinements to the model, and then applies those refinements in detail to the Afghan case. In so doing, it shifts the focus from potentially altruistic state-building to a case that was driven by pure national-strategic interests. Issues surrounding the liberal peace ideology dominate recent army reconstruction in conflict-affected states. The liberal peace underpinning is of supreme importance, so much so that in many discussions, it is internalised and accepted virtually without thought. This paper will advance the body of knowledge by establishing, for the first time, a theoretical basis for the widespread failure of army reconstruction in Afghanistan. The empirical basis builds on extensive previous research by other scholars. The resulting model can also be applied to better explain outcomes in other similar cases.     

When is model complexity too much? Illustrating the benefits of simple models with Hughes' salvo equations

The simulations that many defense analysts rely upon in their studies continue to grow in size and complexity. This paper contrasts the guidance that the authors have received—from some of the giants of military operations research—with the current practice. In particular, the analytic utility of Hughes' simple salvo equations is compared with that of the complex Joint Warfighting System (JWARS), with respect to JWARS' key performance parameters. The comparison suggests that a family of analytic tools supports the best analyses. It follows that smaller, more agile, and transparent models, such as Hughes' salvo equations, are underutilized in defense analyses. We believe that these models should receive more attention, use, and funding. To illustrate this point, this paper uses two very simple models (by modern standards) to rapidly generate insights on the value of information relative to force strength. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2003     

A note on the limitations of goal programming as observed in resource allocation for marine environmental protection

After first formulating the problem of the Marine Environmental Protection program of the Coast Guard as a multiple-objective linear program, we investigate the applicability and limitations of goal programming. We point out how the preemptive goal-programming approach is incompatible with utility preferences. Then we observe the tendency of optimal solutions for standard linear goal programs to occur at extreme points. We also note problems of more general approaches, such as dealing with additively separable approximations to preferences.