Neighborhood search heuristics for selecting hierarchically well‐formulated subsets in polynomial regression

The importance of subset selection in multiple regression has been recognized for more than 40 years and, not surprisingly, a variety of exact and heuristic procedures have been proposed for choosing subsets of variables. In the case of polynomial regression, the subset selection problem is complicated by two issues: (1) the substantial growth in the number of candidate predictors, and (2) the desire to obtain hierarchically well‐formulated subsets that facilitate proper interpretation of the regression parameter estimates. The first of these issues creates the need for heuristic methods that can provide solutions in reasonable computation time; whereas the second requires innovative neighborhood search approaches that accommodate the hierarchical constraints. We developed tabu search and variable neighborhood search heuristics for subset selection in polynomial regression. These heuristics are applied to a classic data set from the literature and, subsequently, evaluated in a simulation study using synthetic data sets. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

Putin – the masked nemesis of the strategy of ambiguity

Recent events demonstrate the complex and adaptive approach employed by Russia to reassert influence in Europe. The changing face of Russia’s strategy commenced in 2007 when it launched a crippling cyber-attack against Estonia. This was followed by a large Russian conventional attack against Georgia in 2008, occupying two large areas of the nation. 2014 witnessed the Russian annexation of Crimea where in just a week, Russia seized control of Crimea “without firing a shot.” The annexation of Crimea was rapidly followed by a Russian inspired and led subversive war in eastern Ukraine. The common thread among these diverse Russian operations is its use of ambiguity to confound and confuse decision makers in the West.     

MIDDLE EAST NONPROLIFERATION

The Middle East is a crucial region for the global nonproliferation regime. In 2010, the state parties to the Treaty on the Non-Proliferation of Nuclear Weapons proposed a conference on a Middle East Weapons of Mass Destruction-Free Zone. The nuclear weapon-free zone model, on which this idea builds, has achieved important results in other regions, but faces especially stark challenges in the Middle East. However, the attempt to apply the boldly imaginative zone approach to the Middle East holds promise for building a more inclusive dialogue on nonproliferation and regional security.     

Strategies for managing the flexible capacity in the airline industry

The ability to effectively match supply and demand under uncertainty can result in significant revenue benefits in the airline industry. We study the benefits of a Demand Driven Swapping (DDS) approach that takes advantage of the flexibilities in the system and dynamically swaps aircraft as departures near and more accurate demand information is obtained. We analyze the effectiveness of different DDS strategies, characterized by their frequency (how often the swapping decision is revised), in hedging against demand uncertainty. Swapping aircraft several weeks prior to departures will not cause much disturbance to revenue management and operations, but will be based on highly uncertain demands. On the other hand, revising the swapping decision later will decrease the possibility of bad swaps, but at a higher cost of disrupting airport services and operations. Our objective is to provide guidelines on how the flexible (swappable) capacity should be managed in the system. We study analytical models to gain insights into the critical parameters that affect the revenue benefits of the different swapping strategies. Our study determines the conditions under which each of the different DDS strategies is effective. We complement our analysis by testing the proposed DDS strategies on a set of flight legs, using data obtained from United Airlines. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004.     

An analysis of constrained robust regression estimators

Multicollinearity and nonnormal errors are problems often encountered in the application of linear regression. Estimators are proposed for dealing with the simultaneous occurrence of both multicollinearity and nonnormality. These estimators are developed by combining biased estimation techniques with certain robust criteria. An iteratively reweighted least-squares procedure is used to compute the estimates. The performance of the combined estimators is studied empirically through Monte Carlo experiments structured according to factorial designs. With respect to a mean-squared-error criterion, the combined estimators are superior to ordinary least-squares, pure biased estimators, and pure robust estimators when multicollinearity and nonnormality are present. The loss in efficiency for the combined estimators relative to least squares is small when these problems do not occur. Some guidelines for the use of these combined estimators are given.     

Multiechelon repairable-item provisioning in a time-varying environment using the randomization technique

Multiechelon repairable-item provisioning systems are considered under a time-varying environment. Such conditions could arise, for example, in a military context where a shift from peacetime operation to wartime operation takes place; or, in a civilian setting where a public transit system decides to increase its hours of operation or frequency of service. Exact Markovian models, incorporating a finite population of repairable components and limited repair capacity (nonample service), are treated, with transient solutions obtained using the randomization technique. The exact models are compared with the approximate Dyna-METRIC model which assumes an infinite population of components and ample repair capacity.     

The effect of nonnormality on variables sampling plans

Acceptance sampling plans based on variables have been in use for many years. Recently, there has been a renewal of interest in these plans, because of the relative efficiencies that they offer with respect to attributes sampling regarding sample size. Furthermore, in situations where acceptable quality levels are very small, and a high level of protection is desired, variables sampling is often much more efficient than attributes sampling. An important disadvantage of variables sampling is that the distribution of the parameter being inspected must be known. Most standard variables sampling plans assume that the distribution of this parameter is normal. This article examines the effect of the normality assumption in variables sampling. Methods to detect departures from normality are reviewed.     

Equity in genetic newborn screening

State‐level newborn screening allows for early treatment of genetic disorders, which can substantially improve health outcomes for newborns. As the cost of genetic testing decreases, it is becoming an essential part of newborn screening. A genetic disorder can be caused by many mutation variants; therefore, an important decision is to determine which variants to search for (ie, the panel design), under a testing budget. The frequency of variants that cause a disorder and the incidence of the disorder vary by racial/ethnic group. Consequently, it is important to consider equity issues in panel design, so as to reduce disparities among different groups. We study the panel design problem using cystic fibrosis (CF) as a model disorder, considering the trade‐offs between equity and accuracy, under a limited budget. Most states use a genetic test in their CF screening protocol, but panel designs vary, and, due to cost, no state's panel includes all CF‐causing variants. We develop models that design equitable genetic testing panels, and compare them with panels that maximize sensitivity in the general population. Our case study, based on realistic CF data, highlights the value of equitable panels and provides important insight for newborn screening practices.     

Waging the inchoate war: Defining,fighting, and second-guessing the ‘Long War’

Abstract

This article answers three questions: What is the nature of the Long War? How is progress (or lack thereof) to be assessed? Where is it likely to go next? An appreciation of Clausewitz shows that practical centers of gravity exist for the Long War, and that the conflict pivots upon the ability to persuasively link ideology to events via a strategic narrative. A close examination of an illustrative case study, the interaction between the US and the late Abu Musab al-Zarqawi in Iraq 2004 – 2006, shows that Al Qaeda has suffered a severe setback, but also that the nature of the war is set to shift yet again. Further tangible progress for the US requires waging the Long War as a global counterinsurgency based on a strategy of ‘selective identification’ (versus pure ‘disaggregation’) as well as an understanding of how to more effectively craft a strategic narrative.