Testing goodness of fit to the increasing failure rate family

One branch of the reliability literature is concerned with devising statistical procedures with various nonparametric “restricted family” model assumptions because of the potential improved operating characteristics of such procedures over totally nonparametric ones. In the single-sample problem with unknown increasing failure rate (IFR) distribution F, (1) maximum-likelihood estimators of F have been calculated, (2) upper or lower tolerance limits for F have been determined, and (3) tests of the null hypothesis that F is exponential have been constructed. Barlow and Campo proposed graphical methods for assessing goodness of fit to the IFR model when the validity of this assumption is unknown. This article proposes several analytic tests of the IFR null hypothesis based on the maximum distance and area between the cumulative hazard function and its greatest convex minorant (GCM), and the maximum distance and area between the total time on test statistic and its GCM. A table of critical points is provided to implement a specific test having good overall power properties.     

Testing goodness of fit to the increasing failure rate family with censored data

One important thrust in the reliability literature is the development of statistical procedures under various “restricted family” model assumptions such as the increasing failure rate (IFR) and decreasing failure rate (DFR) distributions. However, relatively little work has been done on the problem of testing fit to such families as a null hypothesis. Barlow and Campo proposed graphical methods for assessing goodness of fit to the IFR model in single-sample problems. For the same problem with complete data, Tenga and Santner studied several analytic tests of the null hypothesis that the common underlying distribution is IFR versus the alternative that it is not IFR for complete data. This article considers the same problem for the case of four types of censored data: (i) Type I (time) censoring, (ii) Type I1 (order statistic) censoring, (iii) a hybrid of Type I and Type I1 censoring, and (iv) random censorship. The least favorable distributions of several intuitive test statistics are derived for each of the four types of censoring so that valid small-sample-size α tests can be constructed from them. Properties of these tests are investigated.     

Multiple-facility loading under capacity-based economies of scope

Multiple-facility loading (MFL) involves the allocation of products among a set of finite-capacity facilities. Applications of MFL arise naturally in a variety of production scheduling environments. MFL models typically assume that capacity is consumed as a linear function of products assigned to a facility. Product similarities and differences, however, result in capacity-based economies or diseconomies of scope, and thus the effective capacity of the facility is often a (nonlinear) function of the set of tasks assigned to the facility. This article addresses the multiple-facility loading problem under capacity-based economies (and diseconomies) of scope (MFLS). We formulate MFLS as a nonlinear 0–1 mixed-integer programming problem, and we discuss some useful properties. MFLS generalizes many well-known combinatorial optimization problems, such as the capacitated facility location problem and the generalized assignment problem. We also define a tabu-search heuristic and a branch-and-bound algorithm for MFLS. The tabu-search heuristic alternates between two search phases, a regional search and a diversification search, and offers a novel approach to solution diversification. We also report computational experience with the procedures. In addition to demonstrating MFLS problem tractability, the computational results indicate that the heuristic is an effective tool for obtaining high-quality solutions to MFLS. © 1997 John Wiley & Sons, Inc. Naval Research Logistics 44: 229–256, 1997     

Hybrid War and Its Countermeasures: A Critique of the Literature

This article examines critically the literature of hybrid war and evaluates the countermeasures often proposed. It explains the concept of hybrid warfare and its varied interpretations, illustrating how it is a manifestation of current anxieties in armed conflict. The selection of the literature is based on works that are referenced, that offer a scientific approach, and which review either the phenomenon of hybrid warfare or its countermeasures empirically. Unscientific works have been omitted. The analysis of the literature presented here shows that the antidotes to ‘hybridity’ lie not in the operational or tactical sphere but in strategic and political domains.     

Combating nuclear smuggling? Exploring drivers and challenges to detecting nuclear and radiological materials at maritime facilities

International concern over nuclear terrorism has grown during the past few decades. This has driven a broad spectrum of efforts to strengthen nuclear security globally, including the widespread adoption of radiation-detection technology for border monitoring. Detection systems are now deployed at strategic locations for the purported purpose of detecting and deterring the smuggling of nuclear and radioactive materials. However, despite considerable investment in this area, few studies have examined how these programs are implemented or the operational challenges they face on a day-to-day basis. This article seeks to address this with a focus on radiation-detection efforts at maritime facilities. Utilizing practitioner interviews and a survey, this article identifies the factors that influence the planning and use of these systems in this fast-moving environment. The results clearly demonstrate that the implementation of these systems varies significantly across different national and organizational contexts, resulting in a fragmented global nuclear-detection architecture, which arguably undermines efforts to detect trafficked nuclear-threat materials. Greater consideration should therefore be given to developing international standards and guidance, designing and adopting tools to support key parts of the alarm assessment process, and broader sharing of good practice.