Using stochastic programming to solve an outpatient appointment scheduling problem with random service and arrival times

We study a stochastic outpatient appointment scheduling problem (SOASP) in which we need to design a schedule and an adaptive rescheduling (i.e., resequencing or declining) policy for a set of patients. Each patient has a known type and associated probability distributions of random service duration and random arrival time. Finding a provably optimal solution to this problem requires solving a multistage stochastic mixed‐integer program (MSMIP) with a schedule optimization problem solved at each stage, determining the optimal rescheduling policy over the various random service durations and arrival times. In recognition that this MSMIP is intractable, we first consider a two‐stage model (TSM) that relaxes the nonanticipativity constraints of MSMIP and so yields a lower bound. Second, we derive a set of valid inequalities to strengthen and improve the solvability of the TSM formulation. Third, we obtain an upper bound for the MSMIP by solving the TSM under the feasible (and easily implementable) appointment order (AO) policy, which requires that patients are served in the order of their scheduled appointments, independent of their actual arrival times. Fourth, we propose a Monte Carlo approach to evaluate the relative gap between the MSMIP upper and lower bounds. Finally, in a series of numerical experiments, we show that these two bounds are very close in a wide range of SOASP instances, demonstrating the near‐optimality of the AO policy. We also identify parameter settings that result in a large gap in between these two bounds. Accordingly, we propose an alternative policy based on neighbor‐swapping. We demonstrate that this alternative policy leads to a much tighter upper bound and significantly shrinks the gap.     

A platform for initial testing of multiple camouflage patterns

The human visual system is still an important factor in military warfare; military personnel receive training on effective search strategies, and camouflage that can effectively conceal objects and personnel is a key component of a successful integrated survivability strategy. Previous methods of camouflage assessment have, amongst others, used psychophysics to generate distinctiveness metrics. However, the population from which the human observers are drawn is often not well defined, or necessarily appropriate. In this experiment we designed a new platform for testing multiple patterns based on a camouflaged object detection task, and investigate whether trained military observers perform better than civilians. We use a two-alternative forced choice paradigm, with participants searching images of woodland for a replica military helmet displaying Olive Green, Multi Terrain Pattern, US Marine Pattern or, as a conspicuous control, UN Peacekeeper Blue. Our data show that there is no difference in detection performance between the two observer groups but that there are clear differences in the effectiveness of the different helmet colour patterns in a temperate woodland environment. We conclude that when tasks involve very short stimulus presentation times, task-specific training has little effect on the success of target detection and thus this paradigm is particularly suitable for robust estimates of camouflage efficacy.     

Decision support model for effects estimation and proportionality assessment for targeting in cyber operations

Cyber operations are relatively a new phenomenon of the last two decades.During that period,they have increased in number,complexity,and agility,while their design and development have been processes well kept under secrecy.As a consequence,limited data(sets)regarding these incidents are available.Although various academic and practitioner public communities addressed some of the key points and dilemmas that surround cyber operations(such as attack,target identification and selection,and collateral damage),still methodologies and models are needed in order to plan,execute,and assess them in a responsibly and legally compliant way.Based on these facts,it is the aim of this article to propose a model that i))estimates and classifies the effects of cyber operations,and ii)assesses proportionality in order to support targeting decisions in cyber operations.In order to do that,a multi-layered fuzzy model was designed and implemented by analysing real and virtual realistic cyber operations combined with interviews and focus groups with technical-military experts.The proposed model was evaluated on two cyber operations use cases in a focus group with four technical-military experts.Both the design and the results of the evaluation are revealed in this article.     

Property distribution and configurations of sovereign states: A rational economic model

A nation's wealth is both an object of conquest to covetous aggressors and a resource to its owners for self defense. To maintain autonomy every country must mount a defense which either makes its capture (1) more expensive than any aggressor can afford, or (2) more expensive than it is worth to aggressors. Whether this condition can be satisfied for all countries simultaneously depends as shown in this paper on relative efficacy of military offense versus defense, the aggregate of wealth among nations and its distribution, and the benefits a conqueror may obtain from conquest, including the duration of these benefits. The paper shows how these factors fit together to determine the sustainability and stability of the international distribution of property as embodied in the configuration of sovereign states.     

Exploiting the Challenges to Bioweapons Development

ABSTRACT

Widespread and often exaggerated generalizations about the global spread weapons of mass destruction (WMDs) have proven to be not only misleading and technologically naïve, but also unhelpful in formulating effective policies to counter their threat. The new book by George Mason University's Sonia Ben Ouagrham-Gormley dispels the popular narrative that governments and terror groups can easily—and inevitably will—develop WMDs, particularly biological weapons, by exploring the complex external and internal conditions that such programs require, as demonstrated by the Cold War-era biological weapon programs of the superpowers. This empirically grounded and realistic assessment of how states try—and often fail—to develop such programs offers a more reliable basis to craft realistic counterproliferation policies that can elicit international support.     

External security determinants of Greek military expenditure: An empirical investigation using Neural networks

Greece has regularly ranked as the country with the highest defence burden in NATO and the European Union. Over the past decades she has allocated an averatge 6% of GDP to defence yearly. This study using neural networks examines the external security determinants of Greek military expenditure in the context of the ongoing Greek‐Turkish conflict.     

The United Nations-African Union Mission in Darfur: Implications and prospects for success

With the security situation in Darfur remaining grim, the international community passed United Nations Security Resolution 1769 that authorised a more robust peacekeeping force. This article addresses the security concerns motivating the United Nations-African Union Mission in Darfur (UNAMID), highlights the mandate and implications of the force, and compares the potential command and control issues to the experiences of the Somalia intervention in the 1990s. It closes by analysing the prospects for success of the intervention and offering some limited recommendations on ways to mitigate the risks associated with the peacekeeping effort.