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.     

Real-time prediction of projectile penetration to laminates by training machine learning models withfinite element solver as the trainer

Studies on ballistic penetration to laminates is complicated, but important for design effective protection of structures. Experimental means of study is expensive and can often be dangerous. Numerical simu-lation has been an excellent supplement, but the computation is time-consuming. Main aim of this thesis was to develop and test an effective tool for real-time prediction of projectile penetrations to laminates by training a neural network and a decision tree regression model. A large number of finite element models were developed;the residual velocities of projectiles fromfinite element simulations were used as the target data and processed to produce sufficient number of training samples. Study focused on steel 4340tpolyurea laminates with various configurations. Four different 3D shapes of the projectiles were modeled and used in the training. The trained neural network and decision tree model was tested using independently generated test samples using finite element models. The predicted projectile velocity values using the trained machine learning models are then compared with thefinite element simulation to verify the effectiveness of the models. Additionally, both models were trained using a published experimental data of projectile impacts to predict residual velocity of projectiles for the unseen samples. Performance of both the models was evaluated and compared. Models trained with Finite element simulation data samples were found capable to give more accurate predication, compared to the models trained with experimental data, becausefinite element modeling can generate much larger training set, and thus finite element solvers can serve as an excellent teacher. This study also showed that neural network model performs better with small experimental dataset compared to decision tree regression model.     

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.     

Field test and probabilistic analysis of irregular steel debris casualty risks from a person-borne improvised explosive device

Person-borne improvised explosive devices (PBIEDs) are often used in terrorist attacks in Western countries. This study aims to predict the trajectories of PBIED fragments and the subsequent safety risks for people exposed to this hazard. An explosive field test with a typical PBIED composed of a plastic explosive charge and steel nut enhancements was performed to record initial fragment behaviour, including positions, velocity, and trajectory angles. These data were used to predict the full trajectory of PBIED fragments using a probabilistic analysis. In the probabilistic analyses a probability of fatality or serious injury was computed. Based on the results presented, many practical conclusions can be drawn, for instance, regarding safe evacuation distances if a person were exposed to a suspected PBIED.     

Maximizing project cash availability

Consider a project during the life cycle of which there are cash payouts and in‐flows. To better meet his financial commitments, the project owner would like to meet all deadlines without running out of cash. We show that the cash availability objective is similar to the total weighted flowtime used to measure work‐in‐progress performance in the scheduling and inventory control literatures. In this article we provide several specialized solution methods for the problem of minimizing total weighted flowtime in an arbitrary acyclic project network, subject to activity release times and due dates, where the activity weights may be positive or negative and represent cash in‐ and out‐flows. We describe the structure of an optimal solution and provide several efficient algorithms and their complexity based on mincost and maxflow formulations. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006     

RETROSPECT AND PROSPECT: TRENDS IN COST AND THEIR IMPLICATIONS FOR UK AEROSPACE

This paper, intended to stimulate debate, suggests that we are at a turning point in the history of the UK military aerospace industry. It argues that there is urgent need for a fresh vision as to how the UK military aerospace industry can both prosper and make its maximum contribution to the defence of Britain. Further, the resulting plans and policies must emphasise the robustness of defence capabilities in a future in which the only thing we can be certain is that it will be very different from the present – especially whenever defence most matters.     

Models of scientific and technological review for the Biological and Toxin Weapons Convention

ABSTRACT

Science and technology (S&T) review is key to anticipating developments in the life sciences that may benefit or run contrary to the aims of the 1972 Biological and Toxin Weapons Convention. It serves as a mechanism for both preparing against novel biological threats and identifying the best opportunities for developing and sharing the life sciences to the fullest extent. In the age of rapidly advancing biotechnology, S&T review needs to be wide ranging, involve a diverse set of inputs, and be transparent about its methods and data. This viewpoint considers four models of S&T review and their capacity to respond to the challenge of the life sciences: standing advisory boards; ad hoc working groups; peer review; and wikis. It then identifies a hybrid model that is suitably broad, diverse, and transparent.     

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.     

Concept costing for defence projects: the problem and its solution

It is intrinsic to the nature of military competition that the unit acquisition costs of defence systems rise generation by generation. Traditional “bottom‐up” methods of cost‐estimating cannot meet the increasing demands for extensive studies of many options before commencing design and, while “top‐down” estimating techniques are an improvement, in particular by reducing the time required for preparing estimates, they also are inadequate to meet current requirements. A new method has been devised, therefore, further developing “top‐down” methods using Bayesian techniques to make best use of the available information, whether certain or uncertain, and its accuracy established by example.