CALLING FOR ACTION

International Atomic Energy Agency (IAEA) safeguards are under more stress today than at any time in their history. Compliance concerns, a shortage of resources and technology, and growing responsibilities threaten to undermine the effectiveness and credibility of this vital and fundamental pillar of the nonproliferation regime. To address this challenge, the United States recently launched the Next Generation Safeguards Initiative. The goal of this initiative is to ensure the IAEA makes the fullest possible use of its existing authority to prevent the diversion of safeguarded material and to investigate suspicious activities. The initiative will advance state-of-the-art technology, foster the development of a new generation of safeguards experts, and promote technology collaborations and safeguards-conscious infrastructure in states using or pursuing nuclear power. Although it has a domestic focus, the initiative's intent is to catalyze a much broader commitment to international safeguards in partnership with other governments and the IAEA.     

Securing a border under asymmetric information

We study a stochastic interdiction model of Morton et al. IIE Transactions, 39 (2007):3–14 that locates radiation sensors at border crossings to detect and prevent the smuggling of nuclear material. In this model, an interdictor places sensors at customs checkpoints to minimize a potential smuggler's maximum probability of crossing a border undetected. We focus on a model variant in which the interdictor has different, and likely more accurate, perceptions of the system's parameters than the smuggler does. We introduce a model that is tighter and uses fewer constraints than that of Morton et al. We also develop a class of valid inequalities along with a corresponding separation procedure that can be used within a cutting‐plane approach to reduce computational effort. Computational results demonstrate the effectiveness of our approach.Copyright © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 91–100, 2014     

A mathematical formulation and efficient heuristics for the dynamic container relocation problem

The container relocation problem (CRP) is concerned with emptying a single yard‐bay which contains J containers each following a given pickup order so as to minimize the total number of relocations made during their retrieval process. The CRP can be modeled as a binary integer programming (IP) problem and is known to be NP‐hard. In this work, we focus on an extension of the CRP to the case where containers are both received and retrieved from a single yard‐bay, and call it the dynamic container relocation problem. The arrival (departure) sequences of containers to (from) the yard‐bay is assumed to be known a priori. A binary IP formulation is presented for the problem. Then, we propose three types of heuristic methods: index based heuristics, heuristics using the binary IP formulation, and a beam search heuristic. Computational experiments are performed on an extensive set of randomly generated test instances. Our results show that beam search heuristic is very efficient and performs better than the other heuristic methods.Copyright © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 101–118, 2014     

End effects in capacity expansion models with finite horizons

Capacity expansion models are typically formulated in the context of some finite horizon. Because the firm lasts longer than the horizon, a bias can enter into the optimal solution from the model horizon chosen. Recently, Grinold [8] has proposed a “dual-equilibrium method” for ameliorating possible distortions. Although the dual-equilibrium method has superior analytical properties to other methods, it is conceptually more complex. In this paper it is shown that there are situations where the “primal-equilibrium” approach of Manne [15] provides equivalent results and that the use of annualized capital costs in the objective function, although somewhat less efficient, results in a similar model.     

A stochastic network formulation for complex sequential processes

A network model incorporating stochastic features is considered. The model represents a complex sequential process where an object or system moves through a succession of states (nodes) and operating modes (classes) in the course of carrying out its function (fulfilling its purpose). Transitions between states and operating modes occur in a possibly random manner and require (consume) some resource in randomly varying amounts. We discuss the routing behavior and resource requirements of a typical object as it moves through (and eventually out of) the network. We then shift our focus from a single object and its odyssey to the network as a whole, where time is the resource and many objects are entering the network according to a possibly nonhomogeneous Poisson pattern; in this vein, we discuss the evolution of the network over time. Finally, we consider some applications of the formulation, and results.     

On estimating population characteristics from record-breaking observations. i. parametric results

Consider an experiment in which only record-breaking values (e.g., values smaller than all previous ones) are observed. The data available may be represented as X₁,K₁,X₂,K₂, …, where X₁,X₂, … are successive minima and K₁,K₂, … are the numbers of trials needed to obtain new records. We treat the problem of estimating the mean of an underlying exponential distribution, and we consider both fixed sample size problems and inverse sampling schemes. Under inverse sampling, we demonstrate certain global optimality properties of an estimator based on the “total time on test” statistic. Under random sampling, it is shown than an analogous estimator is consistent, but can be improved for any fixed sample size.     

The procurement problem: An integer programming problem well suited to a solution using duality

A procurement problem, as formulated by Murty [10], is that of determining how many pieces of equipment units of each of m types are to be purchased and how this equipment is to be distributed among n stations so as to maximize profit, subject to a budget constraint. We have considered a generalization of Murty's procurement problem and developed an approach using duality to exploit the special structure of this problem. By using our dual approach on Murty's original problem, we have been able to solve large problems (1840 integer variables) with very modest computational effort. The main feature of our approach is the idea of using the current evaluation of the dual problem to produce a good feasible solution to the primal problem. In turn, the availability of good feasible solutions to the primal makes it possible to use a very simple subgradient algorithm to solve the dual effectively.