The Demand for Military Expenditure in Europe: The Role of Fiscal Space in the Context of a Resurgent Russia

A variant of established work on the demand for military expenditure is developed based on a practical concept of fiscal space from the perspective of short-term government choices concerning public expenditures. A new indicator, referred to as fiscal capacity, is defined and used as a candidate explanatory variable in an empirical model of European defence spending over the 2007–2016 period. Fiscal capacity is found to outperform simpler measurements of economic conditions, notably GDP growth forecasts, in explaining changes in defence spending efforts as a share of GDP. Regarding security environment variables, the results suggest that Russia has recently come to be seen as a potential military threat by European nations, leading to defence spending increases, the more so the shorter the distance to stationed or deployed Russian forces, and particularly so by those European nations that have a land border with Russia. A prospective exercise is then carried out in order to assess the capacity of EU member states that are also members of NATO to reach NATO’s 2% goal for defence spending over a mid-term horizon.     

A stochastic air combat logistics decision model for Blue versus Red opposition

Technologically advanced aircraft rely on robust and responsive logistics systems to ensure a high state of operational readiness. This paper fills a critical gap in the literature for combat models by closely relating effectiveness of the logistics system to determinants of success in combat. We present a stochastic diffusion model of an aerial battle between Blue and Red forces. The number of aircraft of Blue forces aloft and ready to be aloft on combat missions is limited by the maximum number of assigned aircraft, the reliability of aircraft subsystems, and the logistic system's ability to repair and replenish those subsystems. Our parsimonious model can illustrate important trade‐offs between logistics decision variables and operational success.     

The optimal linear combination of control variates in the presence of asymptotically negligible bias

The optimal linear combination of control variates is well known when the controls are assumed to be unbiased. We derive here the optimal linear combination of controls in the situation where asymptotically negligible bias is present. The small-sample linear control which minimizes the mean square error (MSE) is derived. When the optimal asymptotic linear control is used rather than the optimal small-sample control, the degradation in MSE is c/n³, where n is the sample size and c is a known constant. This analysis is particulary relevant to the small-sample theory for control variates as applied to the steady-state estimation problem. Results for the method of multiple estimates are also given.     

Optimizing the allocation of components to kits in small-lot,multiechelon assembly systems

The kitting problem in multiechelon assembly systems is to allocate on-hand stock and anticipated future deliveries to kits so that cost is minimized. This article structures the kitting problem and describes several preprocessing methods that are effective in refining the formulation. The model is resolved using an optimizing approach based on Lagrangian relaxation, which yields a separable problem that decomposes into a subproblem for each job. The resulting subproblems are resolved using a specialized dynamic programming algorithm, and computational efficiency is enhanced by dominance properties devised for that purpose. The Lagrangian problem is resolved effectively using subgradient optimization and a specialized branching method incorporated in the branch-and-bound procedure. Computational experience demonstrates that the specialized approach outperforms the general-purpose optimizer OSL. The new solution approach facilitates time-managed flow control, prescribing kitting decisions that promote cost-effective performance to schedule. © 1994 John Wiley & Sons. Inc.     

A note on the optimality conditions for the bilevel programming problem

A counterexample is given to demonstrate that previously proposed necessary conditions for the bilevel programming problem are not correct. An interpretation of the difficulty is given by appealing to a “theorem of alternative” result presented in the original work.     

Scheduling economic lot sizes in deteriorating production systems

The paper considers the economic lot scheduling problem (ELSP) where production facility is assumed to deteriorate, owing to aging, with an increasing failure rate. The time to shift from an “in‐control” state to an “out‐of‐control” state is assumed to be normally distributed. The system is scheduled to be inspected at the end of each production lot. If the process is found to be in an “out‐of‐control” state, then corrective maintenance is performed to restore it to an “in‐control” state before the start of the next production run. Otherwise, preventive maintenance is carried out to enhance system reliability. The ELSP is formulated under the capacity constraint taking into account the quality related cost due to possible production of non‐conforming items, process inspection, and maintenance costs. In order to find a feasible production schedule, both the common cycle and time‐varying lot sizes approaches are utilized. © 2003 Wiley Periodicals, Inc. Naval Research Logistics 50: 650–661, 2003     

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     

Justifying Cyber-intelligence?

The surge in threats aided by or carried out through cyberspace has placed significant pressure on the intelligence community to adapt or leave itself open to attack. Indeed, many in both political and intelligence circles argue for access to ever greater amounts of cyber information in order to catch potential threats before they become real. By collecting all our digital information, the intelligence community argues that it is not only able to detail what people have done or are currently doing but also predict what their next move might be. However, the ethical implications are unclear and the backlash following Edward Snowden’s revelations have shown that such activities are not without controversy. This leaves the debate stuck between the important, ethical role that intelligence can play and the potential for its unrestrained use to cause undue harm. This paper will resolve this by giving greater detail to cyber-intelligence practices, highlighting the different levels of harm that the various intelligence operations can cause. The essence of this paper is not that cyber-intelligence should be banned outright, but that it can be justified given the necessary circumstances. Therefore, the paper will develop a specialised set of Just Cyber-Intelligence Principles, built on the just war tradition, to outline if and when such activities are justified.