A trajectory for homeland ballistic missile defense

The Ground-based Midcourse Defense system is intended to protect the US homeland against limited attacks from intermediate- and long-range ballistic missiles. It has succeeded in intercepting target missiles and can engage a threat launched from North Korea or the Middle East, targeting any point in the USA. Nevertheless, high-profile struggles and program changes related to homeland ballistic missile defense (BMD) continue to make headlines. The most significant struggle has been a string of three straight intercept test failures over five years, followed by the recent successful intercept test in June 2014. This article first briefly reviews the current threats of concern. It then examines homeland BMD policy objectives, followed by the current major technical issues in supporting these objectives and, then, the likelihood of negating a warhead. Finally, it highlights major considerations that should be part of the trajectory the US government takes moving forward.     

THE MORAL PERMISSIBILITY OF AUTOMATED RESPONSES DURING CYBERWARFARE

Automated responses are an inevitable aspect of cyberwarfare, but there has not been a systematic treatment of the conditions in which they are morally permissible. We argue that there are three substantial barriers to the moral permissibility of an automated response: the attribution, chain reaction, and projection bias problems. Moreover, these three challenges together provide a set of operational tests that can be used to assess the moral permissibility of a particular automated response in a specific situation. Defensive automated responses will almost always pass all three challenges, while offensive automated responses typically face a substantial positive burden in order to overcome the chain reaction and projection bias challenges. Perhaps the most interesting cases arise in the middle ground between cyber-offense and cyber-defense, such as automated cyber-exploitation responses. In those situations, much depends on the finer details of the response, the context, and the adversary. Importantly, however, the operationalizations of the three challenges provide a clear guide for decision-makers to assess the moral permissibility of automated responses that could potentially be implemented.     

A three‐stage procurement optimization problem under uncertainty

This article examines a problem faced by a firm procuring a material input or good from a set of suppliers. The cost to procure the material from any given supplier is concave in the amount ordered from the supplier, up to a supplier‐specific capacity limit. This NP‐hard problem is further complicated by the observation that capacities are often uncertain in practice, due for instance to production shortages at the suppliers, or competition from other firms. We accommodate this uncertainty in a worst‐case (robust) fashion by modeling an adversarial entity (which we call the “follower”) with a limited procurement budget. The follower reduces supplier capacity to maximize the minimum cost required for our firm to procure its required goods. To guard against uncertainty, the firm can “protect” any supplier at a cost (e.g., by signing a contract with the supplier that guarantees supply availability, or investing in machine upgrades that guarantee the supplier's ability to produce goods at a desired level), ensuring that the anticipated capacity of that supplier will indeed be available. The problem we consider is thus a three‐stage game in which the firm first chooses which suppliers' capacities to protect, the follower acts next to reduce capacity from unprotected suppliers, and the firm then satisfies its demand using the remaining capacity. We formulate a three‐stage mixed‐integer program that is well‐suited to decomposition techniques and develop an effective cutting‐plane algorithm for its solution. The corresponding algorithmic approach solves a sequence of scaled and relaxed problem instances, which enables solving problems having much larger data values when compared to standard techniques. © 2013 Wiley Periodicals, Inc. Naval Research Logistics, 2013     

A note on scheduling parallel machines subject to breakdown and repair

In this paper we consider n jobs and a number of machines in parallel. The machines are identical and subject to breakdown and repair. The number may therefore vary over time and is at time t equal to m(t). Preemptions are allowed. We consider three objectives, namely, the total completion time, ∑ C_j, the makespan C_max, and the maximum lateness L_max. We study the conditions on m(t) under which various rules minimize the objective functions under consideration. We analyze cases when the jobs have deadlines to meet and when the jobs are subject to precedence constraints. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2004.     

A generalization of the weighted set covering problem

We study a generalization of the weighted set covering problem where every element needs to be covered multiple times. When no set contains more than two elements, we can solve the problem in polynomial time by solving a corresponding weighted perfect b‐matching problem. In general, we may use a polynomial‐time greedy heuristic similar to the one for the classical weighted set covering problem studied by D.S. Johnson [Approximation algorithms for combinatorial problems, J Comput Syst Sci 9 (1974), 256–278], L. Lovasz [On the ratio of optimal integral and fractional covers, Discrete Math 13 (1975), 383–390], and V. Chvatal [A greedy heuristic for the set‐covering problem, Math Oper Res 4(3) (1979), 233–235] to get an approximate solution for the problem. We find a worst‐case bound for the heuristic similar to that for the classical problem. In addition, we introduce a general type of probability distribution for the population of the problem instances and prove that the greedy heuristic is asymptotically optimal for instances drawn from such a distribution. We also conduct computational studies to compare solutions resulting from running the heuristic and from running the commercial integer programming solver CPLEX on problem instances drawn from a more specific type of distribution. The results clearly exemplify benefits of using the greedy heuristic when problem instances are large. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2005     

A generalized assignment model for dynamic supply chain capacity planning

We consider a generalization of the well‐known generalized assignment problem (GAP) over discrete time periods encompassed within a finite planning horizon. The resulting model, MultiGAP, addresses the assignment of tasks to agents within each time period, with the attendant single‐period assignment costs and agent‐capacity constraint requirements, in conjunction with transition costs arising between any two consecutive periods in which a task is reassigned to a different agent. As is the case for its single‐period antecedent, MultiGAP offers a robust tool for modeling a wide range of capacity planning problems occurring within supply chain management. We provide two formulations for MultiGAP and establish that the second (alternative) formulation provides a tighter bound. We define a Lagrangian relaxation‐based heuristic as well as a branch‐and‐bound algorithm for MultiGAP. Computational experience with the heuristic and branch‐and‐bound algorithm on over 2500 test problems is reported. The Lagrangian heuristic consistently generates high‐quality and in many cases near‐optimal solutions. The branch‐and‐bound algorithm is also seen to constitute an effective means for solving to optimality MultiGAP problems of reasonable size. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012     

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     

The Damoclean sword of offensive cyber: Policy uncertainty and collective insecurity

Cyberspace is a new domain of operation, with its own characteristics. Cyber weapons differ qualitatively from kinetic ones: They generate effects by non-kinetic means through information, technology, and networks. Their properties, opportunities, and constraints are comparable to the qualitative difference between conventional and nuclear weapons. New weapons and their target sets in a new domain raise a series of unresolved policy challenges at the domestic, bilateral, and international levels about deterrence, attribution, and response. They also introduce new risks: uncertainty about unintended consequences, expectations of efficacy, and uncertainty about both the target’s and the international community’s response. Cyber operations offer considerable benefits for states to achieve strategic objectives both covertly and overtly. However, without a strategic framework to contain and possibly deter their use, make state and non-state behavior more predictable in the absence of reciprocal norms, and limit their impact, an environment where states face persistent attacks that nonetheless fall below the threshold of armed conflict presents a policy dilemma that reinforces collective insecurity.