Class scheduling algorithms for Navy training schools

The problem of developing good schedules for Navy C-Schools has been modeled as a combinatorial optimization problem. The only complicating feature of the problem is that classes must be grouped together into sequences known as pipelines. An ideal schedule will have all classes in a pipeline scheduled in consecutive weeks. The objective is to eliminate the nonproductive time spent by sailors at C-Schools who are waiting for the next class in a pipeline. In this investigation an implicit enumeration procedure for this problem was developed. The key component of our algorithm is a specialized greedy algorithm which is used to obtain a good initial incumbent. Often this initial incumbent is either an optimal schedule or a near optimal schedule. In an empirical analysis with the only other competing software system, our greedy heuristic found equivalent or better solutions in substantially less computer time. This greedy heuristic was extended and modified for the A-School scheduling problem and was found to be superior to its only competitor. © 1998 John Wiley & Sons, Inc. Naval Research Logistics 45: 533–551, 1998     

The Imperfect Peace: Explaining Paramilitary Violence in Northern Ireland

This article examines why paramilitary campaigns in Northern Ireland have continued despite the current peace process. In advancing three instrumental explanations of paramilitary violence, the author provides a useful analytical framework which shows that while organizational interpretations are dominating the public discourse, republicans and loyalists continue to use paramilitary violence as a strategic means of threat and coercion. Furthermore, even though policy implications are not discussed in detail, it will be demonstrated that the dominance of organizational imperatives has resulted in excessive leniency by the state, which - in turn - has contributed to the fragility of the peace process.     

开放架构的数字视频管理系统iView研究与实现

iView是我们研制的基于多模态元数据提取,具有开放架构且支持无线移动存取的数字视频全内容管理系统。本文讨论了系统的需求,体系框架设计实现,描述了其中涉及的关键技术思想,并对存在的问题和研究方向做了简单探讨。     

Rent seeking and defence expenditure

A plethora of arguments from the ‘public choice school’ suggest that public goods are ‘under‐supplied’ by comparison with public sector provision of more private goods. The implication is that rent seeking by the ‘military‐industrial complex’ serves, in some measure, to offset potential allocative inefficiency. In this paper a comparison of rent seeking by producers of public goods and producers of private goods identifies a bias that favours public good supply. The bias results from the different way in which collective demand for public goods and for private goods is expressed. The prospect of larger rents to the ‘military‐industrial complex’ implies greater rent‐seeking expenditures in this sector. The extent to which public goods (and, in particular, defence) are ‘under supplied’ is much exaggerated.     

Screening multi-dimensional heterogeneous populations for infectious diseases under scarce testing resources,with application to COVID-19

Testing provides essential information for managing infectious disease outbreaks, such as the COVID-19 pandemic. When testing resources are scarce, an important managerial decision is who to test. This decision is compounded by the fact that potential testing subjects are heterogeneous in multiple dimensions that are important to consider, including their likelihood of being disease-positive, and how much potential harm would be averted through testing and the subsequent interventions. To increase testing coverage, pooled testing can be utilized, but this comes at a cost of increased false-negatives when the test is imperfect. Then, the decision problem is to partition the heterogeneous testing population into three mutually exclusive sets: those to be individually tested, those to be pool tested, and those not to be tested. Additionally, the subjects to be pool tested must be further partitioned into testing pools, potentially containing different numbers of subjects. The objectives include the minimization of harm (through detection and mitigation) or maximization of testing coverage. We develop data-driven optimization models and algorithms to design pooled testing strategies, and show, via a COVID-19 contact tracing case study, that the proposed testing strategies can substantially outperform the current practice used for COVID-19 contact tracing (individually testing those contacts with symptoms). Our results demonstrate the substantial benefits of optimizing the testing design, while considering the multiple dimensions of population heterogeneity and the limited testing capacity.     

Satellite constellation design for forest fire monitoring via a stochastic programing approach

There is significant value in the data collected by satellites during and after a natural disaster. The current operating paradigm in practice is for satellites to passively collect data when they happen to fly over a disaster location. Conversely, this article considers the alternative approach of actively maneuvering satellites to fly directly overhead of the disaster site on a routine basis. Toward this end, we seek to compute a satellite constellation design that minimizes the expected maneuver costs for monitoring an unknown forest fire. In this article, we present a 2‐stage stochastic programing model for this problem as well as a accelerated L‐shaped decomposition approach. A comparison between our approach and the current operating paradigm indicates that our solution provides longer duration data collections and a greater number of data collections. Analysis also shows that our proposed solution is robust over a wide array of scenarios.